quantitative research topics about social media

234 Social Media Research Topics & Ideas

Icon Calendar 18 May 2024
Icon Page 2646 words
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Social media research encompasses a broad range of different topics that delve into the ever-evolving digital landscape. People investigate the impact of social platforms on society, exploring subjects, such as online identity formation, self-presentation, the psychology of virtual interactions, and others. Additionally, studies examine the influence of social media on politics, activism, and public opinion, uncovering patterns of information dissemination and polarization. Privacy concerns, cyberbullying, and online safety are also explored in-depth, seeking strategies to mitigate the associated risks. In this article, people can find many social media research topics, ideas, and examples.

Hot Social Media Research Topics

Impacts of Social Media and Internet Algorithms on User Experience
The Rise of TikTok: A Socio-Cultural Analysis
Dealing With Cyberbullying: Strategies and Solutions
Understanding the Phenomenon of Social Media ‘Cancel Culture’
NFTs and Social Media: The Future of Digital Art?
Ethical Concerns in the Era of Influencer Marketing
Social Media’s Role in Accelerating E-Commerce Growth
Impacts of Internet and Social Media on Journalism and News Reporting
Understanding the Psychology of Viral Challenges on Social Platforms
Cryptocurrency and Social Media: The Intersection
Mitigating Misinformation and ‘Fake News’ on Social Media
Augmented Reality (AR) in Social Media: A Game Changer?
Evaluating the Impact of Social Media on Political Campaigns
Social Media’s Influence on Fashion and Beauty Trends
Privacy, Safety, and Security Concerns in the Age of Social Networking
Roles of Free Access and Social Media in Promoting Sustainable Practices
Implications of Social Media Addiction on Mental Health
Examining Social Media’s Role in Crisis Communication
The Power of User-Generated Content in Branding
Influence of Social Media on Food Culture and Dining Trends

Easy Social Media Research Topics

Impacts of Online Videos and Social Media on Mental Health
Influencer Marketing: Efficacy and Ethical Concerns
Evolution of Privacy Policies Across Social Platforms
Understanding Virality: What Makes Content Shareable?
Cyberbullying: Prevalence and Prevention Strategies
Social Media and Political Polarization: An In-Depth Study
Role of Social Media in Modern Business Strategies
Effect of Social Media on Interpersonal Relationships
Social Platforms as Tools for Social Change
Navigating Online Hate Speech: A Legal Perspective
Emerging Trends in Social Media Advertising
Online Identity Construction and Self-Presentation
The Psychology of Social Media Addiction
Social Media’s Role in Crisis Management and Communication
Sentiment Analysis in Social Media and Its Implications
Social Media Algorithms: Bias and Implications
The Phenomenon of Cancel Culture on Social Platforms
Cybersecurity Threats in the Era of Social Media
Analyzing Adverse Impacts of Social Media on Consumer Behavior

Interesting Social Media Research Topics

Evaluating the Effects of Social Media on Language and Communication
Roles of Social Media in Fostering Political Engagement
Misinformation and Propaganda Spread Through Social Platforms
Analyzing the Shift From Traditional Media to Social Media
Dark Patterns in Social Media: Hidden Manipulative Tactics
Social Media and Digital Activism: Revolutionizing Advocacy
Augmented Reality (AR) and Its Impact on Social Networking
Exploring Cybersecurity Issues in Social Media Platforms
Roles and Effects of Social Media and News in Mental Health Promotion
Strategies for Effective Social Media Crisis Management
The Power of Live Streaming for Brands and Influencers
Using Social Media to Enhance Classroom Learning
Analyzing the Influence of Memes on Internet Culture
Impacts of Social Media Algorithms on User Behavior
Assessing the Correlation Between Social Media and Loneliness
Geotagging and Its Implications for Personal Privacy
Social Media and E-commerce: A Cross-Industry Study
The Ethics of Digital Advertising on Social Platforms
Understanding the Psychology of Social Media Trolls
The Cultural Shift Caused by Social Media Localization

Social Media Research Paper Topics for High School

The Phenomenon of Cyberbullying: Prevention and Strategies
How Does Social Media Influence Teen Body Image?
Evaluating the Educational Potential of Social Media Platforms
Impacts of Social Media on Adolescents’ Self-Esteem
Roles of Free Connection and Social Media in Modern Political Activism
Exploring the Concept of ‘Digital Citizenship’ Among Teenagers
The Ethics of Social Media Privacy: User Rights and Responsibilities
Social Media Addiction: Understanding Its Causes and Effects
Influence of Social Media on Modern Communication Styles
Analyzing Positive Roles of Social Media in Promoting Reading Culture
Social Media and Mental Health: Correlation or Causation?
The Role of Social Media in Global Environmental Awareness
Examining Social Media’s Impact on Real-Life Social Skills
Social Media Platforms: Tools for Personal Branding or Narcissism?
Influence of Social Media Trends on Youth Fashion Choices
Impacts of Social Media on Teenagers’ Sleep Patterns
Online Safety: The Role of Parents and Schools in Social Media Usage
How Does Social Media Influence Teenagers’ Views on Relationships?
Social Media and Empathy: Does Online Interaction Decrease Compassion?

Social Media Research Paper Topics for College Students

Evaluating the Impact of Social Media on Body Image and Self-Esteem
The Influence of Social Media on Voting Patterns Among Young Adults
Social Media as a Valid Tool for Social Change: A Case Study Approach
Unveiling the Psychology of Social Media Addiction
Social Media’s Role in Modern Journalism: Opportunities and Challenges
Privacy Implications of Data Collection on Social Media Platforms
Cyberbullying in the Age of Social Media: Scope and Solutions
The Ethical Aspects of Social Media Influencer Marketing
Roles and Effects of Social Media in Crisis Communication and Management
Social Media and Its Effects on Interpersonal Communication Skills
Analyzing Social Media Strategies of Successful Businesses
Impacts of Internet Use and Social Media on Mental Health Among College Students
The Roles That Social Media Has in Modern Political Campaigns
Understanding the Social Media Algorithm: Bias and Implications
Social Media and Consumer Behavior: The Power of Influencer Marketing
Fake News, Authors, and Disinformation Spread Through Social Media Platforms
Exploring Direct Links Between Social Media Use and Academic Performance
Social Media’s Role in Promoting Sustainable Lifestyle Choices
Regulation of Hate Speech and Offensive Content on Social Media
The Power and Peril of Virality in the Age of Social Media

Social Media Research Paper Topics for University

The Effect That Social Media Has on Global Politics
The Ethics of Data Mining in Social Media
Roles of Social Media in Business Marketing Strategies
Social Media, Internet Use, and Their Impacts on Mental Health: A Systematic Review
Algorithmic Bias in Social Media Platforms: Causes and Consequences
The Influence of Colors and Social Media on Consumer Behavior
Exploring Possible Relationships Between Social Media Use and Academic Performance
Privacy, Morality, and Security Concerns in the Age of Social Media
Social Media as a Platform for Digital Activism
Impacts of Social Media on Interpersonal Communication and Relationships
Cyberbullying on Social Media: Scope, Impact, and Preventive Measures
The Role of Social Media in Spreading Health-Related Misinformation
Analyzing the Effect of Social Media on Journalism Practices
Understanding the Influence of Social Media on Body Image Perceptions
Social Media’s Role in Crisis Management: Case Studies
The Power and Effectiveness of Influencer Marketing on Social Media
Fake News and Disinformation in the Social Media Age
Regulatory Approaches to Hate Speech on Social Media Platforms
The Economic Implications of Social Media: From Startups to Giants

Social Media Research Paper Topics for Masters

Advanced Algorithms and Their Role in Shaping Social Media Interactions
Evaluating the Impact of Social Media on Democratic Processes Globally
The Intersection of Privacy, Data Mining, and Ethics in Social Media
Quantitative Analysis of Social Media’s Impact on Consumer Buying Behavior
Cybersecurity Threats in Social Media: Mitigation and Prevention Strategies
Analyzing the Psychological Implications of Social Media Addiction
Using Social Media Data to Predict Market Trends: An Econometric Approach
Role of Social Media in Crisis Management: A Comparative Study
The Sociolinguistic Impact of Social Media on Communication
Machine Learning and AI in Social Media: An Examination of Emerging Trends
Social Media as a Valid Tool for Public Health: Opportunities and Challenges
Social Media’s Influence on Modern Journalism: A Critical Analysis
Mapping Social Networks: A Graph Theory Approach
Evaluating the Efficacy of Social Media Campaigns in Social Change Movements
Analyzing the Role of Social Media in Corporate Reputation Management
Data Privacy Laws and Social Media: A Comparative Study
The Use of Small and Big Data Analytics in Social Media Marketing
Social Media and Its Role in Strengthening Democracy: A Deep Dive
The Impact of Social Media on Cultural Assimilation and Identity
Ethics of Artificial Intelligence in Social Media Content Moderation

Social Media Research Paper Topics for Ph.D.

Analyzing the Impact of Social Media Algorithms on User Behavior and Perceptions
Deciphering the Influence of Social Media on Political Campaign Strategies
Examining the Role of Social Media in Corporate Social Responsibility Initiatives
Social Media and Mental Health: A Comprehensive Analysis of Recent Studies
Effects of Social Media and Internet Use on Consumer Buying Behavior: An Econometric Approach
Social Media and Digital Diplomacy: A Critical Analysis
Ethical Implications of Data Mining Techniques in Social Media Platforms
Unpacking the Psychological Mechanisms of Social Media Addiction
Role of Social Media in Contemporary Journalism: Opportunities and Challenges
Social Media and Privacy: A Comparative Study of Data Protection Laws
Machine Learning and AI in Social Media: Identifying Future Trends
Social Media’s Possible Influence on People, Body Image, and Self-Esteem: A Meta-Analysis
Analyzing the Role of Social Media in Crisis Management and Communication
Impacts of Social Media on Different Language and Communication Styles
Cybersecurity in Social Media: An Analysis of Current Threats and Mitigation Strategies
Social Media as a Good Tool for Health Promotion and Disease Prevention
Effects of Social Media on Children and Their Parents: Social Skills and Interpersonal Relationships
Roles of Social Media in Promoting Gender Equality and Women’s Rights
Social Media and its Influence on Cultural Assimilation and Identity Formation

Social Media Research Topics for Argumentative Papers

Impacts of Social Media on Social and Political Discourses: Enhancing or Hindering Democratic Engagement?
Social Media and Mental Health: Exploring the Association Between Excessive Usage and Psychological Well-Being
Fostering Online Activism and Social Movements: The Role of Social Media
Balancing Personal Information Sharing and Data Protection: Social Media and Privacy
Exploring the Effects of Social Media on Body Image and Self-Esteem
Social Media and Political Polarization: Reinforcing Echo Chambers or Encouraging Diverse Perspectives?
Youth Culture and Identity Formation: The Influence of Social Media
Fake News and Misinformation: Combating Inaccurate Information in the Era of Social Media
Social Media and Cyberbullying: Examining the Impact on Mental Health and Well-Being
The Ethics of Social Media Research: Privacy, Informed Consent, and Ethical Considerations
Relationships in the Digital Age: Exploring the Influence of Social Media Use
The Influence of Internet, Technology, and Social Media on Consumer Behavior and Buying Decisions
Analyzing the Role of Online Platforms in Elections: Social Media and Political Campaigns
Social Media in Education: Exploring the Benefits and Challenges of Integration in the Classroom
Impacts of Social Media and Interface on News Consumption and Journalism Practices
Body Politics in the Digital Space: Examining Representations of Gender, Race, and Body Image on Social Media
Addressing Ethical and Security Concerns in the Digital Age: Social Media and Cybersecurity
Shaping Consumer Behavior and Brand Perception: The Role of Social Media Influencers
Civic Engagement in the Digital Era: Assessing the Role of Social Media Platforms
The Influence of Social Media Algorithms on Information Consumption and Personalization

Social Media Research Topics for Persuasive Papers

The Power of Social Media in Driving Social and Political Change
Promoting Digital Literacy: Empowering Users to Navigate the Complexities of Social Media
Social Media as a Catalyst for Social Justice Movements: Amplifying Marginalized Voices
Countering Fake News and Misinformation on Social Media: Strategies for Critical Thinking
Harnessing the Influence of Social Media for Environmental Activism and Sustainability
The Dark Side of Social Media: Addressing Online Harassment and Cyberbullying
Influencer Marketing: Ethical Considerations and Consumer Protection in the Digital Age
Leveraging Social Media for Public Health Campaigns: Increasing Awareness and Behavioral Change
Social Media and Mental Health: Promoting Well-Being in a Hyperconnected World
Navigating the Privacy Paradox: Balancing Convenience and Personal Data Protection on Social Media
Roles of Social Media and Internet in Fostering Civic Engagement and Democratic Participation
Promoting Positive Body Image on Social Media: Redefining Beauty Standards and Empowering Individuals
Enhancing Online Safety: Developing Policies and Regulations for Social Media Platforms
Social Media and the Spread of Disinformation: Combating the Infodemic
Roles of Social Media and Technology in Building and Sustaining Relationships: Connecting in a Digital Era
Influencer Culture and Materialism: Examining the Impact on Consumer Behavior
Social Media and Education: Maximizing Learning Opportunities and Bridging the Digital Divide
The Power of Viral Hashtags: Exploring Social Movements and Online Activism
Social Media and Political Polarization: Bridging Divides and Encouraging Constructive Dialogue

Social Media Topics for Pros and Cons Research Papers

Examining the Social Effects of Digital Connectivity: Pros and Cons of Using Social Media
Balancing Privacy Concerns in the Digital Age: Evaluating the Cons and Risks of Social Media Use
Information Sharing in the Digital Era: Uncovering the Advantages of Social Media Platforms
Building Online Communities: Analyzing the Strengths and Weaknesses of Social Media Interaction
Navigating Political Discourse in the Digital Age: The Disadvantages of Social Media Engagement
Mental Health in the Digital Sphere: Understanding the Benefits and Drawbacks of Social Media
Combating Cyberbullying: Addressing the Negative Side of Online Social Interactions
Personal Branding in the Digital Landscape: Empowerment vs. Self-Objectification on Social Media
Establishing Meaningful Connections: Exploring the Pros and Cons of Social Media Relationships
Leveraging the Educational Potential of Digital Platforms: Examining the Benefits of Social Media in Learning
Body Image and Self-Esteem in the Age of Social Media: Weighing the Positives and Negatives
From Digital Activism to Political Change: Assessing the Opportunities and Limitations of Social Media
Unraveling the Influence: Social Media and Consumer Behavior in the Digital Marketplace
Misinformation in the Digital Landscape: The Pros and Cons of Social Media in the Spread of Disinformation
Crisis Communication in the Digital Age: Navigating the Benefits and Challenges of Social Media
Tackling Fake News: Navigating Misinformation in the Era of Social Media
Maximizing Business Opportunities: Evaluating the Advantages and Disadvantages of Social Media Marketing
The Psychology of Social Media: Analyzing the Upsides and Downsides of Digital Engagement
Exploring the Impact of Social Media on Socialization: Benefits, Drawbacks, and Implications
Online Activism: The Power and Limitations of Social Media Movements

Social Media Topics for Cause and Effect Research Papers

Enhancing Political Activism: Exploring the Relationship Between Social Media and Civic Engagement
The Psychological Effects of Digital Connectivity: Investigating the Relationship Between Mental Health of People and Social Media Use
Political Polarization in the Online Sphere: Understanding the Impact of Digital Networks
Disrupted Sleep Patterns in the Digital Era: Exploring the Role of Online Platforms
Digital Distractions and Academic Performance: Analyzing the Effects of Online Engagement
Navigating Online Relationships: Understanding the Impacts of Digital Interactions
The Digital Marketplace: Exploring Consumer Behavior in the Age of Online Platforms
The Loneliness Epidemic: Investigating the Relationship Between Social Media Use and Social Isolation
Redefining Political Participation: The Influence of Digital Networks on Democracy
Unmasking Digital Identities: The Psychological Effects of Social Media Use
News Consumption in the Digital Era: Exploring the Impacts of Online Platforms
Cyberbullying in the Virtual World: Analyzing the Effects of Online Interactions
The Digital Campaign Trail: Investigating the Influence of Online Platforms on Voter Behavior
Fear of Missing Out (FOMO) in the Digital Age: Exploring the Psychological Consequences
Body Dissatisfaction in the Digital Sphere: Understanding the Impacts of Online Presence
Information Overload: Coping With the Digital Deluge in the Information Age
Privacy Concerns in the Online Landscape: Analyzing the Implications of Digital Footprints
Unveiling the Dark Side: Exploring the Relationship Between Online Activities and Substance Abuse
Bridging the Political Divide: The Impact of Digital Networks on Sociopolitical Polarization

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13 social media research topics to explore in 2024

Last updated

15 January 2024

Reviewed by

Miroslav Damyanov

Short on time? Get an AI generated summary of this article instead

To help you choose a specific area to examine, here are some of the top social media research topics that are relevant in 2024.

What makes a strong social media research topic?

Consider the factors below to ensure your topic is strong and compelling:

Clarity: regardless of the topic you investigate, clarity is essential. It ensures readers will be able to understand your work and any wider learnings. Your argument should be clear and your language unambiguous.

Trend relevancy: you need to know what’s currently happening in social media to draw relevant conclusions. Before choosing a topic, consider current popular platforms, trending content, and current use cases to ensure you understand social media as it is today.

New insights: if your research is to be new, innovative, and helpful for the wider population, it should cover areas that haven’t been studied before. Look into what’s already been thoroughly researched to help you uncover knowledge gaps that could be good focus areas.

Tips for choosing social media research topics

When considering social media research questions, it’s also important to consider whether you’re the right person to conduct that area of study. Your skills, interests, and time allocated will all impact your suitability.

Consider your skillset: your specific expertise is highly valuable when conducting research. Choosing a topic that aligns with your skills will help ensure you can add a thorough analysis and your own learnings.

Align with your interests: if you’re deeply interested in a topic, you’re much more likely to enjoy the process and dedicate the time it needs for a thorough analysis.

Consider your resources: the time you have available to complete the research, your allocated funds, and access to resources should all impact the research topic you choose.

13 social media research paper topics

To help you choose the right area of research, we’ve rounded up some of the most compelling topics within the sector. These ideas may also help you come up with your own.

1. The influence of social media on mental health

It’s well-documented that social media can impact mental health. For example, a significant amount of research has highlighted the link between social media and conditions like anxiety, depression, and stress—but there’s still more to uncover in this area.

There are high rates of mental illness worldwide, so there’s continual interest in ways to understand and mitigate it. Studies could focus on the following areas:

The reasons why social media can impact mental health

How social media can impact specific mental health conditions (you might also look at different age groups here)

How to reduce social media’s impact on mental health

2. The effects of social media exposure on child development

There are many unknowns with social media. More research is needed to understand how it impacts children. As such, this is a very valuable research area.

You might explore the following topics:

How social media impacts children at different ages

The long-term effects of childhood social media use

The benefits of social media use in children

How social media use impacts childhood socialization, communication, and learning

3. The role of social media in political campaigning

Social media’s role in political campaigning is nothing new. The Cambridge Analytica Scandal, for example, involved data from millions of Facebook profiles being sold to a third party for political advertising. Many believe this could have impacted the 2016 US election results. Ultimately, Facebook had to pay a private class-action lawsuit of $725 million.

The role of social media in political campaigns is of global significance. Concerns are still high that social media can play a negative role in elections due to the spread of misinformation, disinformation, and the bandwagon effect.

Research in this area could look into the following topics:

How people are influenced by social media when it comes to voting

Ways to mitigate misinformation

Election interference and how this can be prevented

4. The role of social media in misinformation and disinformation

Misinformation and disinformation mean slightly different things. Misinformation is unintentionally sharing false or inaccurate information, while disinformation is sharing false information with the deliberate intent to mislead people.

Both can play a role not just in elections but throughout social media. This became particularly problematic during the COVID-19 pandemic.

Research into this area is important given the widespread risk that comes with spreading false information about health and safety-related topics.

Here are some potential research areas:

How misinformation and disinformation are spread via social media

The impact of false information (you could focus on how it impacts health, for example)

Strategies for mitigating the impact of false information and encouraging critical thinking

The avenues through which to hold technology companies accountable for spreading misinformation

5. The impact of AI and deepfakes on social media

AI technology is expected to continue expanding in 2024. Some are concerned that this could impact social media. One concern is the potential for the widespread use of deepfake technology—a form of AI that uses deep learning to create fake images.

Fake images can be used to discredit, shame, and control others, so researchers need to deeply understand this area of technology. You might look into the following areas:

The potential impacts of deepfakes on businesses and their reputations

Deepfake identities on social media: privacy concerns and other risks

How deepfake images can be identified, controlled, and prevented

6. How social media can benefit communities

While there’s much research into the potential negative impacts of social media, it can also provide many benefits.

Social media can establish connections for those who might otherwise be isolated in the community. It can facilitate in-person gatherings and connect people who are physically separated, such as relatives who live in different countries. Social media can also provide critical information to communities quickly in the case of emergencies.

Research into the ways social media can provide these key benefits can make interesting topics. You could consider the following:

Which social media platforms offer the most benefits

How to better use social media to lean into these benefits

How new social platforms could connect us in more helpful ways

7. The psychology of social media

Social media psychology explores human behavior in relation to social media. There are a range of topics within social media psychology, including the following:

The influence of social media on social comparison

Addiction and psychological dependence on social media

How social media increases the risk of cyberbullying

How social media use impacts people’s attention spans

Social interactions and the impact on socialization

Persuasion and influence on social media

8. How communication has evolved through social media

Social media has provided endless ways for humans to connect and interact, so the ways we do this have evolved.

Most obviously, social media has provided ways to connect instantaneously via real-time messaging and communicate using multimedia formats, including text, images, emojis, video content, and audio.

This has made communication more accessible and seamless, especially given many people now own smartphones that can connect to social media apps from anywhere.

You might consider researching the following topics:

How social media has changed the way people communicate

The impacts of being continuously connected, both positive and negative

How communication may evolve in the future due to social media

9. Social media platforms as primary news sources

As social media use has become more widespread, many are accessing news information primarily from their newsfeeds. This can be particularly problematic, given that newsfeeds are personalized providing content to people based on their data.

This can cause people to live in echo chambers, where they are constantly targeted with content that aligns with their beliefs. This can cause people to become more entrenched in their way of thinking and more unable or unwilling to see other people’s opinions and points of view.

Research in this area could consider the following:

The challenges that arise from using social media platforms as a primary news source

The pros and cons of social media: does it encourage “soloization” or diverse perspectives?

How to prevent social media echo chambers from occurring

The impact of social media echo chambers on journalistic integrity

10. How social media is impacting modern journalism

News platforms typically rely on an advertising model where more clicks and views increase revenue. Since sensationalist stories can attract more clicks and shares on social media, modern journalism is evolving.

Journalists are often rewarded for writing clickbait headlines and content that’s more emotionally triggering (and therefore shareable).

Your research could cover the following areas:

How journalism is evolving due to social media

How to mitigate social media’s impact on neutral reporting

The importance of journalistic standards in the age of social media

11. The impact of social media on traditional advertising

Digital advertising is growing in popularity. Worldwide, ad spending on social media was expected to reach $207.1 billion in 2023 . Experts estimate that ad spending on mobile alone will reach $255.8 billion by 2028 . This move continues to impact traditional advertising, which takes place via channels like print, TV, and radio.

Most organizations consider their social strategy a critical aspect of their advertising program. Many exclusively advertise on social media—especially those with limited budgets.

Here are some interesting research topics in this area

The impact of different advertising methods

Which social media advertising channels provide the highest return on investment (ROI)

The societal impacts of social media advertising

12. Impacts of social media presence on corporate image

Social media presence can provide companies with an opportunity to be visible and increase brand awareness . Social media also provides a key way to interact with customers.

More and more customers now expect businesses to be online. Research shows that 63% of customers expect companies to offer customer service via their social media channels, while a whopping 90% have connected with a brand or business through social media.

Research in this area could focus on the following topics:

The advantages and disadvantages of social media marketing for businesses

How social media can impact a business’s corporate image

How social media can boost customer experience and loyalty

13. How social media impacts data privacy

Using social media platforms is free for the most part, but users have to provide their personal data for the privilege. This means data collection , tracking, the potential for third parties to access that data, psychological profiling, geolocation, and tracking are all potential risks for users.

Data security and privacy are of increasing interest globally. Research within this area will likely be in high demand in 2024.

Here are some of the research topics you might want to consider in this area:

Common privacy concerns with social media use

Why is social media privacy important?

What can individuals do to protect their data when using social media?

The importance of social media research

As social media use continues to expand in the US and around the world, there’s continual interest in research on the topic. The research you conduct could positively impact many groups of people.

Topics can cover a broad range of areas. You might look at how social media can harm or benefit people, how social media can impact journalism, how platforms can impact young people, or the data privacy risks involved with social media use. The options are endless, and new research topics will present themselves as technology evolves.

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The Top 10 Most Interesting Social Media Research Topics

Finding social media research topics you’re interested in is tricky. Social media is a fairly new field, and the constant arrival of new technology means that it’s always evolving. So, students have a lot to think about in their search for topics.

In this article, we’re going to walk you through social media research paper topics that are timely and relevant. We’ll also show you examples of social media research topics you can get inspiration from. Lastly, we’re going to lay out some social media research questions you can ponder while formulating your topic.

Find your bootcamp match

What makes a strong social media research topic.

A strong social media research topic requires clarity of focus. This means that your topic must be timely, relevant, and coherent. This allows your research topic to be compelling and easily understandable to others.

Tips for Choosing a Social Media Research Topic

Know the trends. Learning what social media topics are trending allows you to know the relevant issues and emergent themes in the field of social media. This also lets you know what topics are well-researched and which ones are still emerging.
Explore knowledge gaps. Knowing what previous researchers have written prevents you from repeating knowledge that has already been explored and shared. Nobody wants to reinvent the wheel when doing research. Exploring knowledge gaps lets you increase the impact of your work and identify opportunities for further research.
Choose something that you’re interested in. Diving deep into a topic that you’re interested in motivates you to learn more about it. The research process becomes more engaging when you know you care about your topic.
Be specific. Knowing what you want to research and what you don’t want to research are keys to the research process. This entails narrowing down your topic to a specific area, subject, theme, or relationship. You want to know the scope and the limitations of your study.
Check your timeframe. Limiting your topic to a specific timeframe helps in narrowing down what you need to study. For example, you can decide to study a phenomenon that has emerged in just the last three years. By doing this, you’re making sure that your research is both specific and relevant.

What’s the Difference Between a Research Topic and a Research Question?

The difference between a research topic and a research question is in the scope. Research topics tend to be broader than research questions. Research topics focus on a specific area of study within a larger field, while a research question further narrows down what you are researching. A good research question allows you to write on your topic with greater precision.

How to Create Strong Social Media Research Questions

The key to creating strong social media research questions is learning enough about your topic to know where the gaps are. This means that you have to conduct a thorough social media literature review, reading previous studies until you have a handle on what’s been said and what questions are still unanswered. Your question will emerge from this preliminary research.

2. The Influence of Social Media on Mental Health

Mental health has been an important topic in social media research these past few years. Social media use and its connection to mental health has even been the subject of systematic reviews. This means that there’s a huge body of previous studies that you can look to when developing your research question.

Exploring both the positive effects and negative impacts of social media sites on mental health helps people and firms establish guidelines that help user communities. This research topic might also cover strategies for helping social media users improve their mental health.

3. The Role of Social Media in Political Campaigning

Social media is a new tool for political campaigning. Exploring what social media strategies have been conducted by politicians running for office helps in determining how social media aids in political campaigning. Studying new strategies like user-generated content for political campaigning allows you to know how voters interact with political candidates.

4. The Role of Social Media in Disinformation

The rise of fake news has coincided with the rise of social networking websites. This topic involves dissecting how social media technologies allow certain types of online content to thrive and make it easier for bad actors to spread disinformation.

5. How Social Media Can Benefit Communities

More and more social issues have been popularized through online content. Diving deep into how social media can facilitate organizational networking lets you compare the traditional and new organizing strategies being created in digital spaces. It also lets you understand how social media activity influences trends in virtual communities.

6. The Effects of Social Media Exposure on Child Development

Children also use social media sites. Some children use social networking sites under the supervision of their parents, and some do not. Social interaction, online or not, affects how children develop. Studying the psychological effects of social media exposure lets you know how social media may improve or derail the growth of children.

7. How Communication Has Evolved Through Social Media

Body language, tone of voice, and other non-verbal cues are absent in online forms of communication. In their place, emojis and other new ways to express thoughts and emotions have appeared. Learning how social media changes the way we talk to one another allows you to develop a theory of communication that takes into account the role of digital communities.

8. Social Media Platforms as Primary News Sources

A lot of people now are getting their daily dose of news and current events through social media. News networks have also established their social media presence on platforms that they can use to deliver news and current events to their audiences. Researching this topic lets you investigate the changes and innovations in information dissemination.

9. How Social Media Paves Way for Non-Traditional Advertising

Regular social media posts, advertisements, and other forms of online content aren’t the only ways businesses market to their audiences. Social media has paved the way for user-generated content and other non-traditional types of online marketing. With this topic, you can learn social media marketing strategies that have been capitalized on the social connection fostered by social networking websites.

10. Impacts of Social Media Presence on Corporate Image

More businesses increasingly build and curate their digital presence through various social networks. Knowing how a business can improve its corporate image through social media influence clarifies the role of technology in modern economics and online marketing.

Social Media Research Questions

How was Facebook used as a political campaigning tool in the 2020 United States presidential election?
What social platforms are the most effective in influencing consumer behavior?
How does user-generated content boost the credibility of a business?
How do different types of online content disseminated through popular networks affect the attention span of people?
What are the most effective forms of online content and social media strategies for increasing sales conversions for small businesses?

Choosing the Right Social Media Research Topic

Choosing the right social media research topic helps you create meaningful contributions to the discipline of social media studies. Knowing the most popular topics in the field can make you an expert on social media. By reading up on previous studies, you will not only be more informed but you will also be in a position to make a positive impact on future studies.

Studying the relationship between social media and different fields produces valuable knowledge. Even if you’re only interested in exploring one social platform or a single social media event or phenomenon, your research can help people better understand how social media engagement changes the face of social relationships in the world at large.

Social Media Research Topics FAQ

Social media is a computer-based technology that allows digital communities to exchange information through user networks. Various social media networks specialize in text, photo, or video transfer. All of these are ways for people on the Internet to share information and ideas with each other.

Social media research is important because it helps you contribute to the growing body of knowledge about digital social settings. In 2021, according to DataReportal, at least 4.88 billion people around the world use the Internet . The more that people connect with each other through the social media domain, the more their quality of life changes, for better or worse.

According to Statista, the most popular social media platforms right now are Facebook, YouTube, and WhatsApp , each of which has at least two billion users. These social networks allow users to share text, picture, and video content with one another.

People use social media to connect with each other, share information, and entertain themselves. Social media sites can broadly serve all of these purposes or be focused on just one of these functions.

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500+ Quantitative Research Titles and Topics

Table of Contents

Quantitative research involves collecting and analyzing numerical data to identify patterns, trends, and relationships among variables. This method is widely used in social sciences, psychology , economics , and other fields where researchers aim to understand human behavior and phenomena through statistical analysis. If you are looking for a quantitative research topic, there are numerous areas to explore, from analyzing data on a specific population to studying the effects of a particular intervention or treatment. In this post, we will provide some ideas for quantitative research topics that may inspire you and help you narrow down your interests.

Quantitative Research Titles

Quantitative Research Titles are as follows:

Business and Economics

“Statistical Analysis of Supply Chain Disruptions on Retail Sales”
“Quantitative Examination of Consumer Loyalty Programs in the Fast Food Industry”
“Predicting Stock Market Trends Using Machine Learning Algorithms”
“Influence of Workplace Environment on Employee Productivity: A Quantitative Study”
“Impact of Economic Policies on Small Businesses: A Regression Analysis”
“Customer Satisfaction and Profit Margins: A Quantitative Correlation Study”
“Analyzing the Role of Marketing in Brand Recognition: A Statistical Overview”
“Quantitative Effects of Corporate Social Responsibility on Consumer Trust”
“Price Elasticity of Demand for Luxury Goods: A Case Study”
“The Relationship Between Fiscal Policy and Inflation Rates: A Time-Series Analysis”
“Factors Influencing E-commerce Conversion Rates: A Quantitative Exploration”
“Examining the Correlation Between Interest Rates and Consumer Spending”
“Standardized Testing and Academic Performance: A Quantitative Evaluation”
“Teaching Strategies and Student Learning Outcomes in Secondary Schools: A Quantitative Study”
“The Relationship Between Extracurricular Activities and Academic Success”
“Influence of Parental Involvement on Children’s Educational Achievements”
“Digital Literacy in Primary Schools: A Quantitative Assessment”
“Learning Outcomes in Blended vs. Traditional Classrooms: A Comparative Analysis”
“Correlation Between Teacher Experience and Student Success Rates”
“Analyzing the Impact of Classroom Technology on Reading Comprehension”
“Gender Differences in STEM Fields: A Quantitative Analysis of Enrollment Data”
“The Relationship Between Homework Load and Academic Burnout”
“Assessment of Special Education Programs in Public Schools”
“Role of Peer Tutoring in Improving Academic Performance: A Quantitative Study”

Medicine and Health Sciences

“The Impact of Sleep Duration on Cardiovascular Health: A Cross-sectional Study”
“Analyzing the Efficacy of Various Antidepressants: A Meta-Analysis”
“Patient Satisfaction in Telehealth Services: A Quantitative Assessment”
“Dietary Habits and Incidence of Heart Disease: A Quantitative Review”
“Correlations Between Stress Levels and Immune System Functioning”
“Smoking and Lung Function: A Quantitative Analysis”
“Influence of Physical Activity on Mental Health in Older Adults”
“Antibiotic Resistance Patterns in Community Hospitals: A Quantitative Study”
“The Efficacy of Vaccination Programs in Controlling Disease Spread: A Time-Series Analysis”
“Role of Social Determinants in Health Outcomes: A Quantitative Exploration”
“Impact of Hospital Design on Patient Recovery Rates”
“Quantitative Analysis of Dietary Choices and Obesity Rates in Children”

Social Sciences

“Examining Social Inequality through Wage Distribution: A Quantitative Study”
“Impact of Parental Divorce on Child Development: A Longitudinal Study”
“Social Media and its Effect on Political Polarization: A Quantitative Analysis”
“The Relationship Between Religion and Social Attitudes: A Statistical Overview”
“Influence of Socioeconomic Status on Educational Achievement”
“Quantifying the Effects of Community Programs on Crime Reduction”
“Public Opinion and Immigration Policies: A Quantitative Exploration”
“Analyzing the Gender Representation in Political Offices: A Quantitative Study”
“Impact of Mass Media on Public Opinion: A Regression Analysis”
“Influence of Urban Design on Social Interactions in Communities”
“The Role of Social Support in Mental Health Outcomes: A Quantitative Analysis”
“Examining the Relationship Between Substance Abuse and Employment Status”

Engineering and Technology

“Performance Evaluation of Different Machine Learning Algorithms in Autonomous Vehicles”
“Material Science: A Quantitative Analysis of Stress-Strain Properties in Various Alloys”
“Impacts of Data Center Cooling Solutions on Energy Consumption”
“Analyzing the Reliability of Renewable Energy Sources in Grid Management”
“Optimization of 5G Network Performance: A Quantitative Assessment”
“Quantifying the Effects of Aerodynamics on Fuel Efficiency in Commercial Airplanes”
“The Relationship Between Software Complexity and Bug Frequency”
“Machine Learning in Predictive Maintenance: A Quantitative Analysis”
“Wearable Technologies and their Impact on Healthcare Monitoring”
“Quantitative Assessment of Cybersecurity Measures in Financial Institutions”
“Analysis of Noise Pollution from Urban Transportation Systems”
“The Influence of Architectural Design on Energy Efficiency in Buildings”

Quantitative Research Topics

Quantitative Research Topics are as follows:

The effects of social media on self-esteem among teenagers.
A comparative study of academic achievement among students of single-sex and co-educational schools.
The impact of gender on leadership styles in the workplace.
The correlation between parental involvement and academic performance of students.
The effect of mindfulness meditation on stress levels in college students.
The relationship between employee motivation and job satisfaction.
The effectiveness of online learning compared to traditional classroom learning.
The correlation between sleep duration and academic performance among college students.
The impact of exercise on mental health among adults.
The relationship between social support and psychological well-being among cancer patients.
The effect of caffeine consumption on sleep quality.
A comparative study of the effectiveness of cognitive-behavioral therapy and pharmacotherapy in treating depression.
The relationship between physical attractiveness and job opportunities.
The correlation between smartphone addiction and academic performance among high school students.
The impact of music on memory recall among adults.
The effectiveness of parental control software in limiting children’s online activity.
The relationship between social media use and body image dissatisfaction among young adults.
The correlation between academic achievement and parental involvement among minority students.
The impact of early childhood education on academic performance in later years.
The effectiveness of employee training and development programs in improving organizational performance.
The relationship between socioeconomic status and access to healthcare services.
The correlation between social support and academic achievement among college students.
The impact of technology on communication skills among children.
The effectiveness of mindfulness-based stress reduction programs in reducing symptoms of anxiety and depression.
The relationship between employee turnover and organizational culture.
The correlation between job satisfaction and employee engagement.
The impact of video game violence on aggressive behavior among children.
The effectiveness of nutritional education in promoting healthy eating habits among adolescents.
The relationship between bullying and academic performance among middle school students.
The correlation between teacher expectations and student achievement.
The impact of gender stereotypes on career choices among high school students.
The effectiveness of anger management programs in reducing violent behavior.
The relationship between social support and recovery from substance abuse.
The correlation between parent-child communication and adolescent drug use.
The impact of technology on family relationships.
The effectiveness of smoking cessation programs in promoting long-term abstinence.
The relationship between personality traits and academic achievement.
The correlation between stress and job performance among healthcare professionals.
The impact of online privacy concerns on social media use.
The effectiveness of cognitive-behavioral therapy in treating anxiety disorders.
The relationship between teacher feedback and student motivation.
The correlation between physical activity and academic performance among elementary school students.
The impact of parental divorce on academic achievement among children.
The effectiveness of diversity training in improving workplace relationships.
The relationship between childhood trauma and adult mental health.
The correlation between parental involvement and substance abuse among adolescents.
The impact of social media use on romantic relationships among young adults.
The effectiveness of assertiveness training in improving communication skills.
The relationship between parental expectations and academic achievement among high school students.
The correlation between sleep quality and mood among adults.
The impact of video game addiction on academic performance among college students.
The effectiveness of group therapy in treating eating disorders.
The relationship between job stress and job performance among teachers.
The correlation between mindfulness and emotional regulation.
The impact of social media use on self-esteem among college students.
The effectiveness of parent-teacher communication in promoting academic achievement among elementary school students.
The impact of renewable energy policies on carbon emissions
The relationship between employee motivation and job performance
The effectiveness of psychotherapy in treating eating disorders
The correlation between physical activity and cognitive function in older adults
The effect of childhood poverty on adult health outcomes
The impact of urbanization on biodiversity conservation
The relationship between work-life balance and employee job satisfaction
The effectiveness of eye movement desensitization and reprocessing (EMDR) in treating trauma
The correlation between parenting styles and child behavior
The effect of social media on political polarization
The impact of foreign aid on economic development
The relationship between workplace diversity and organizational performance
The effectiveness of dialectical behavior therapy in treating borderline personality disorder
The correlation between childhood abuse and adult mental health outcomes
The effect of sleep deprivation on cognitive function
The impact of trade policies on international trade and economic growth
The relationship between employee engagement and organizational commitment
The effectiveness of cognitive therapy in treating postpartum depression
The correlation between family meals and child obesity rates
The effect of parental involvement in sports on child athletic performance
The impact of social entrepreneurship on sustainable development
The relationship between emotional labor and job burnout
The effectiveness of art therapy in treating dementia
The correlation between social media use and academic procrastination
The effect of poverty on childhood educational attainment
The impact of urban green spaces on mental health
The relationship between job insecurity and employee well-being
The effectiveness of virtual reality exposure therapy in treating anxiety disorders
The correlation between childhood trauma and substance abuse
The effect of screen time on children’s social skills
The impact of trade unions on employee job satisfaction
The relationship between cultural intelligence and cross-cultural communication
The effectiveness of acceptance and commitment therapy in treating chronic pain
The correlation between childhood obesity and adult health outcomes
The effect of gender diversity on corporate performance
The impact of environmental regulations on industry competitiveness.
The impact of renewable energy policies on greenhouse gas emissions
The relationship between workplace diversity and team performance
The effectiveness of group therapy in treating substance abuse
The correlation between parental involvement and social skills in early childhood
The effect of technology use on sleep patterns
The impact of government regulations on small business growth
The relationship between job satisfaction and employee turnover
The effectiveness of virtual reality therapy in treating anxiety disorders
The correlation between parental involvement and academic motivation in adolescents
The effect of social media on political engagement
The impact of urbanization on mental health
The relationship between corporate social responsibility and consumer trust
The correlation between early childhood education and social-emotional development
The effect of screen time on cognitive development in young children
The impact of trade policies on global economic growth
The relationship between workplace diversity and innovation
The effectiveness of family therapy in treating eating disorders
The correlation between parental involvement and college persistence
The effect of social media on body image and self-esteem
The impact of environmental regulations on business competitiveness
The relationship between job autonomy and job satisfaction
The effectiveness of virtual reality therapy in treating phobias
The correlation between parental involvement and academic achievement in college
The effect of social media on sleep quality
The impact of immigration policies on social integration
The relationship between workplace diversity and employee well-being
The effectiveness of psychodynamic therapy in treating personality disorders
The correlation between early childhood education and executive function skills
The effect of parental involvement on STEM education outcomes
The impact of trade policies on domestic employment rates
The relationship between job insecurity and mental health
The effectiveness of exposure therapy in treating PTSD
The correlation between parental involvement and social mobility
The effect of social media on intergroup relations
The impact of urbanization on air pollution and respiratory health.
The relationship between emotional intelligence and leadership effectiveness
The effectiveness of cognitive-behavioral therapy in treating depression
The correlation between early childhood education and language development
The effect of parental involvement on academic achievement in STEM fields
The impact of trade policies on income inequality
The relationship between workplace diversity and customer satisfaction
The effectiveness of mindfulness-based therapy in treating anxiety disorders
The correlation between parental involvement and civic engagement in adolescents
The effect of social media on mental health among teenagers
The impact of public transportation policies on traffic congestion
The relationship between job stress and job performance
The effectiveness of group therapy in treating depression
The correlation between early childhood education and cognitive development
The effect of parental involvement on academic motivation in college
The impact of environmental regulations on energy consumption
The relationship between workplace diversity and employee engagement
The effectiveness of art therapy in treating PTSD
The correlation between parental involvement and academic success in vocational education
The effect of social media on academic achievement in college
The impact of tax policies on economic growth
The relationship between job flexibility and work-life balance
The effectiveness of acceptance and commitment therapy in treating anxiety disorders
The correlation between early childhood education and social competence
The effect of parental involvement on career readiness in high school
The impact of immigration policies on crime rates
The relationship between workplace diversity and employee retention
The effectiveness of play therapy in treating trauma
The correlation between parental involvement and academic success in online learning
The effect of social media on body dissatisfaction among women
The impact of urbanization on public health infrastructure
The relationship between job satisfaction and job performance
The effectiveness of eye movement desensitization and reprocessing therapy in treating PTSD
The correlation between early childhood education and social skills in adolescence
The effect of parental involvement on academic achievement in the arts
The impact of trade policies on foreign investment
The relationship between workplace diversity and decision-making
The effectiveness of exposure and response prevention therapy in treating OCD
The correlation between parental involvement and academic success in special education
The impact of zoning laws on affordable housing
The relationship between job design and employee motivation
The effectiveness of cognitive rehabilitation therapy in treating traumatic brain injury
The correlation between early childhood education and social-emotional learning
The effect of parental involvement on academic achievement in foreign language learning
The impact of trade policies on the environment
The relationship between workplace diversity and creativity
The effectiveness of emotion-focused therapy in treating relationship problems
The correlation between parental involvement and academic success in music education
The effect of social media on interpersonal communication skills
The impact of public health campaigns on health behaviors
The relationship between job resources and job stress
The effectiveness of equine therapy in treating substance abuse
The correlation between early childhood education and self-regulation
The effect of parental involvement on academic achievement in physical education
The impact of immigration policies on cultural assimilation
The relationship between workplace diversity and conflict resolution
The effectiveness of schema therapy in treating personality disorders
The correlation between parental involvement and academic success in career and technical education
The effect of social media on trust in government institutions
The impact of urbanization on public transportation systems
The relationship between job demands and job stress
The correlation between early childhood education and executive functioning
The effect of parental involvement on academic achievement in computer science
The effectiveness of cognitive processing therapy in treating PTSD
The correlation between parental involvement and academic success in homeschooling
The effect of social media on cyberbullying behavior
The impact of urbanization on air quality
The effectiveness of dance therapy in treating anxiety disorders
The correlation between early childhood education and math achievement
The effect of parental involvement on academic achievement in health education
The impact of global warming on agriculture
The effectiveness of narrative therapy in treating depression
The correlation between parental involvement and academic success in character education
The effect of social media on political participation
The impact of technology on job displacement
The relationship between job resources and job satisfaction
The effectiveness of art therapy in treating addiction
The correlation between early childhood education and reading comprehension
The effect of parental involvement on academic achievement in environmental education
The impact of income inequality on social mobility
The relationship between workplace diversity and organizational culture
The effectiveness of solution-focused brief therapy in treating anxiety disorders
The correlation between parental involvement and academic success in physical therapy education
The effect of social media on misinformation
The impact of green energy policies on economic growth
The relationship between job demands and employee well-being
The correlation between early childhood education and science achievement
The effect of parental involvement on academic achievement in religious education
The impact of gender diversity on corporate governance
The relationship between workplace diversity and ethical decision-making
The correlation between parental involvement and academic success in dental hygiene education
The effect of social media on self-esteem among adolescents
The impact of renewable energy policies on energy security
The effect of parental involvement on academic achievement in social studies
The impact of trade policies on job growth
The relationship between workplace diversity and leadership styles
The correlation between parental involvement and academic success in online vocational training
The effect of social media on self-esteem among men
The impact of urbanization on air pollution levels
The effectiveness of music therapy in treating depression
The correlation between early childhood education and math skills
The effect of parental involvement on academic achievement in language arts
The impact of immigration policies on labor market outcomes
The effectiveness of hypnotherapy in treating phobias
The effect of social media on political engagement among young adults
The impact of urbanization on access to green spaces
The relationship between job crafting and job satisfaction
The effectiveness of exposure therapy in treating specific phobias
The correlation between early childhood education and spatial reasoning
The effect of parental involvement on academic achievement in business education
The impact of trade policies on economic inequality
The effectiveness of narrative therapy in treating PTSD
The correlation between parental involvement and academic success in nursing education
The effect of social media on sleep quality among adolescents
The impact of urbanization on crime rates
The relationship between job insecurity and turnover intentions
The effectiveness of pet therapy in treating anxiety disorders
The correlation between early childhood education and STEM skills
The effect of parental involvement on academic achievement in culinary education
The impact of immigration policies on housing affordability
The relationship between workplace diversity and employee satisfaction
The effectiveness of mindfulness-based stress reduction in treating chronic pain
The correlation between parental involvement and academic success in art education
The effect of social media on academic procrastination among college students
The impact of urbanization on public safety services.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Open Access

Peer-reviewed

Research Article

Social impact in social media: A new method to evaluate the social impact of research

Roles Investigation, Writing – original draft

* E-mail: [email protected]

Affiliation Department of Journalism and Communication Studies, Universitat Autonoma de Barcelona, Barcelona, Spain

Affiliation Department of Psychology and Sociology, Universidad de Zaragoza, Zaragoza, Spain

Roles Conceptualization, Investigation, Methodology, Supervision, Writing – review & editing

Affiliation Department of Sociology, Universitat Autonoma de Barcelona, Barcelona, Spain

Affiliation Department of Sociology, Universitat de Barcelona (UB), Barcelona, Spain

Cristina M. Pulido,
Gisela Redondo-Sama,
Teresa Sordé-Martí,
Ramon Flecha

Published: August 29, 2018
https://doi.org/10.1371/journal.pone.0203117
Reader Comments

The social impact of research has usually been analysed through the scientific outcomes produced under the auspices of the research. The growth of scholarly content in social media and the use of altmetrics by researchers to track their work facilitate the advancement in evaluating the impact of research. However, there is a gap in the identification of evidence of the social impact in terms of what citizens are sharing on their social media platforms. This article applies a social impact in social media methodology (SISM) to identify quantitative and qualitative evidence of the potential or real social impact of research shared on social media, specifically on Twitter and Facebook. We define the social impact coverage ratio (SICOR) to identify the percentage of tweets and Facebook posts providing information about potential or actual social impact in relation to the total amount of social media data found related to specific research projects. We selected 10 projects in different fields of knowledge to calculate the SICOR, and the results indicate that 0.43% of the tweets and Facebook posts collected provide linkages with information about social impact. However, our analysis indicates that some projects have a high percentage (4.98%) and others have no evidence of social impact shared in social media. Examples of quantitative and qualitative evidence of social impact are provided to illustrate these results. A general finding is that novel evidences of social impact of research can be found in social media, becoming relevant platforms for scientists to spread quantitative and qualitative evidence of social impact in social media to capture the interest of citizens. Thus, social media users are showed to be intermediaries making visible and assessing evidence of social impact.

Citation: Pulido CM, Redondo-Sama G, Sordé-Martí T, Flecha R (2018) Social impact in social media: A new method to evaluate the social impact of research. PLoS ONE 13(8): e0203117. https://doi.org/10.1371/journal.pone.0203117

Editor: Sergi Lozano, Institut Català de Paleoecologia Humana i Evolució Social (IPHES), SPAIN

Received: November 8, 2017; Accepted: August 15, 2018; Published: August 29, 2018

Copyright: © 2018 Pulido et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: The research leading to these results has received funding from the 7th Framework Programme of the European Commission under the Grant Agreement n° 613202 P.I. Ramon Flecha, https://ec.europa.eu/research/fp7/index_en.cfm . The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

The social impact of research is at the core of some of the debates influencing how scientists develop their studies and how useful results for citizens and societies may be obtained. Concrete strategies to achieve social impact in particular research projects are related to a broader understanding of the role of science in contemporary society. There is a need to explore dialogues between science and society not only to communicate and disseminate science but also to achieve social improvements generated by science. Thus, the social impact of research emerges as an increasing concern within the scientific community [ 1 ]. As Bornmann [ 2 ] said, the assessment of this type of impact is badly needed and is more difficult than the measurement of scientific impact; for this reason, it is urgent to advance in the methodologies and approaches to measuring the social impact of research.

Several authors have approached the conceptualization of social impact, observing a lack of generally accepted conceptual and instrumental frameworks [ 3 ]. It is common to find a wide range of topics included in the contributions about social impact. In their analysis of the policies affecting land use, Hemling et al. [ 4 ] considered various domains in social impact, for instance, agricultural employment or health risk. Moving to the field of flora and fauna, Wilder and Walpole [ 5 ] studied the social impact of conservation projects, focusing on qualitative stories that provided information about changes in attitudes, behaviour, wellbeing and livelihoods. In an extensive study by Godin and Dore [ 6 ], the authors provided an overview and framework for the assessment of the contribution of science to society. They identified indicators of the impact of science, mentioning some of the most relevant weaknesses and developing a typology of impact that includes eleven dimensions, with one of them being the impact on society. The subdimensions of the impact of science on society focus on individuals (wellbeing and quality of life, social implication and practices) and organizations (speeches, interventions and actions). For the authors, social impact “refers to the impact knowledge has on welfare, and on the behaviours, practices and activities of people and groups” (p. 7).

In addition, the terms “social impact” and “societal impact” are sometimes used interchangeably. For instance, Bornmann [ 2 ] said that due to the difficulty of distinguishing social benefits from the superior term of societal benefits, “in much literature the term ‘social impact’ is used instead of ‘societal impact’”(p. 218). However, in other cases, the distinction is made [ 3 ], as in the present research. Similar to the definition used by the European Commission [ 7 ], social impact is used to refer to economic impact, societal impact, environmental impact and, additionally, human rights impact. Therefore, we use the term social impact as the broader concept that includes social improvements in all the above mentioned areas obtained from the transference of research results and representing positive steps towards the fulfilment of those officially defined social goals, including the UN Sustainable Development Goals, the EU 2020 Agenda, or similar official targets. For instance, the Europe 2020 strategy defines five priority targets with concrete indicators (employment, research and development, climate change and energy, education and poverty and social exclusion) [ 8 ], and we consider the targets addressed by objectives defined in the specific call that funds the research project.

This understanding of the social impact of research is connected to the creation of the Social Impact Open Repository (SIOR), which constitutes the first open repository worldwide that displays, cites and stores the social impact of research results [ 9 ]. The SIOR has linked to ORCID and Wikipedia to allow the synergies of spreading information about the social impact of research through diverse channels and audiences. It is relevant to mention that currently, SIOR includes evidence of real social impact, which implies that the research results have led to actual improvements in society. However, it is common to find evidence of potential social impact in research projects. The potential social impact implies that in the development of the research, there has been some evidence of the effectiveness of the research results in terms of social impact, but the results have not yet been transferred.

Additionally, a common confusion is found among the uses of dissemination, transference (policy impact) and social impact. While dissemination means to disseminate the knowledge created by research to citizens, companies and institutions, transference refers to the use of this knowledge by these different actors (or others), and finally, as already mentioned, social impact refers to the actual improvements resulting from the use of this knowledge in relation to the goals motivating the research project (such as the United Nations Sustainable Development Goals). In the present research [ 3 ], it is argued that “social impact can be understood as the culmination of the prior three stages of the research” (p.3). Therefore, this study builds on previous contributions measuring the dissemination and transference of research and goes beyond to propose a novel methodological approach to track social impact evidences.

In fact, the contribution that we develop in this article is based on the creation of a new method to evaluate the evidence of social impact shared in social media. The evaluation proposed is to measure the social impact coverage ratio (SICOR), focusing on the presence of evidence of social impact shared in social media. Then, the article first presents some of the contributions from the literature review focused on the research on social media as a source for obtaining key data for monitoring or evaluating different research purposes. Second, the SISM (social impact through social media) methodology[ 10 ] developed is introduced in detail. This methodology identifies quantitative and qualitative evidence of the social impact of the research shared on social media, specifically on Twitter and Facebook, and defines the SICOR, the social impact coverage ratio. Next, the results are discussed, and lastly, the main conclusions and further steps are presented.

Literature review

Social media research includes the analysis of citizens’ voices on a wide range of topics [ 11 ]. According to quantitative data from April 2017 published by Statista [ 12 ], Twitter and Facebook are included in the top ten leading social networks worldwide, as ranked by the number of active users. Facebook is at the top of the list, with 1,968 million active users, and Twitter ranks 10 th , with 319 million active users. Between them are the following social networks: WhatsApp, YouTube, Facebook Messenger, WeChat, QQ, Instagram,Qzone and Tumblr. If we look at altmetrics, the tracking of social networks for mentions of research outputs includes Facebook, Twitter, Google+,LinkedIn, Sina Weibo and Pinterest. The social networks common to both sources are Facebook and Twitter. These are also popular platforms that have a relevant coverage of scientific content and easy access to data, and therefore, the research projects selected here for application of the SISM methodology were chosen on these platforms.

Chew and Eysenbach [ 13 ] studied the presence of selected keywords in Twitter related to public health issues, particularly during the 2009 H1N1 pandemic, identifying the potential for health authorities to use social media to respond to the concerns and needs of society. Crooks et al.[ 14 ] investigated Twitter activity in the context of a 5.8 magnitude earthquake in 2011 on the East Coast of the United States, concluding that social media content can be useful for event monitoring and can complement other sources of data to improve the understanding of people’s responses to such events. Conversations among young Canadians posted on Facebook and analysed by Martinello and Donelle [ 15 ] revealed housing and transportation as main environmental concerns, and the project FoodRisc examined the role of social media to illustrate consumers’ quick responses during food crisis situations [ 16 ]. These types of contributions illustrate that social media research implies the understanding of citizens’ concerns in different fields, including in relation to science.

Research on the synergies between science and citizens has increased over the years, according to Fresco [ 17 ], and there is a growing interest among researchers and funding agencies in how to facilitate communication channels to spread scientific results. For instance, in 1998, Lubchenco [ 18 ] advocated for a social contract that “represents a commitment on the part of all scientists to devote their energies and talents to the most pressing problems of the day, in proportion to their importance, in exchange for public funding”(p.491).

In this framework, the recent debates on how to increase the impact of research have acquired relevance in all fields of knowledge, and major developments address the methods for measuring it. As highlighted by Feng Xia et al. [ 19 ], social media constitute an emerging approach to evaluating the impact of scholarly publications, and it is relevant to consider the influence of the journal, discipline, publication year and user type. The authors revealed that people’s concerns differ by discipline and observed more interest in papers related to everyday life, biology, and earth and environmental sciences. In the field of biomedical sciences, Haustein et al. [ 20 ] analysed the dissemination of journal articles on Twitter to explore the correlations between tweets and citations and proposed a framework to evaluate social media-based metrics. In fact, different studies address the relationship between the presence of articles on social networks and citations [ 21 ]. Bornmann [ 22 ] conducted a case study using a sample of 1,082 PLOS journal articles recommended in F1000 to explore the usefulness of altmetrics for measuring the broader impact of research. The author presents evidence about Facebook and Twitter as social networks that may indicate which papers in the biomedical sciences can be of interest to broader audiences, not just to specialists in the area. One aspect of particular interest resulting from this contribution is the potential to use altmetrics to measure the broader impacts of research, including the societal impact. However, most of the studies investigating social or societal impact lack a conceptualization underlying its measurement.

To the best of our knowledge, the assessment of social impact in social media (SISM) has developed according to this gap. At the core of this study, we present and discuss the results obtained through the application of the SICOR (social impact coverage ratio) with examples of evidence of social impact shared in social media, particularly on Twitter and Facebook, and the implications for further research.

Following these previous contributions, our research questions were as follows: Is there evidence of social impact of research shared by citizens in social media? If so, is there quantitative or qualitative evidence? How can social media contribute to identifying the social impact of research?

Methods and data presentation

A group of new methodologies related to the analysis of online data has recently emerged. One of these emerging methodologies is social media analytics [ 23 ], which was initially used most in the marketing research field but also came to be used in other domains due to the multiple possibilities opened up by the availability and richness of the data for different research purposes. Likewise, the concern of how to evaluate the social impact of research as well as the development of methodologies for addressing this concern has occupied central attention. The development of SISM (Social Impact in Social Media) and the application of the SICOR (Social Impact Coverage Ratio) is a contribution to advancement in the evaluation of the social impact of research through the analysis of the social media selected (in this case, Twitter and Facebook). Thus, SISM is novel in both social media analytics and among the methodologies used to evaluate the social impact of research. This development has been made under IMPACT-EV, a research project funded under the Framework Program FP7 of the Directorate-General for Research and Innovation of the European Commission. The main difference from other methodologies for measuring the social impact of research is the disentanglement between dissemination and social impact. While altmetrics is aimed at measuring research results disseminated beyond academic and specialized spheres, SISM contribute to advancing this measurement by shedding light on to what extent evidence of the social impact of research is found in social media data. This involves the need to differentiate between tweets or Facebook posts (Fb/posts) used to disseminate research findings from those used to share the social impact of research. We focus on the latter, investigating whether there is evidence of social impact, including both potential and real social impact. In fact, the question is whether research contributes and/or has the potential to contribute to improve the society or living conditions considering one of these goals defined. What is the evidence? Next, we detail the application of the methodology.

Data collection

To develop this study, the first step was to select research projects with social media data to be analysed. The selection of research projects for application of the SISM methodology was performed according to three criteria.

Criteria 1. Selection of success projects in FP7. The projects were success stories of the 7 th Framework Programme (FP7) highlighted by the European Commission [ 24 ] in the fields of knowledge of medicine, public health, biology and genomics. The FP7 published calls for project proposals from 2007 to 2013. This implies that most of the projects funded in the last period of the FP7 (2012 and 2013) are finalized or in the last phase of implementation.

Criteria 2. Period of implementation. We selected projects in the 2012–2013 period because they combine recent research results with higher possibilities of having Twitter and Facebook accounts compared with projects of previous years, as the presence of social accounts in research increased over this period.

Criteria 3. Twitter and Facebook accounts. It was crucial that the selected projects had active Twitter and Facebook accounts.

Table 1 summarizes the criteria and the final number of projects identified. As shown, 10 projects met the defined criteria. Projects in medical research and public health had higher presence.

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https://doi.org/10.1371/journal.pone.0203117.t001

After the selection of projects, we defined the timeframe of social media data extraction on Twitter and Facebook from the starting date of the project until the day of the search, as presented in Table 2 .

https://doi.org/10.1371/journal.pone.0203117.t002

The second step was to define the search strategies for extracting social media data related to the research projects selected. In this line, we defined three search strategies.

Strategy 1. To extract messages published on the Twitter account and the Facebook page of the selected projects. We listed the Twitter accounts and Facebook pages related to each project in order to look at the available information. In this case, it is important to clarify that the tweets published under the corresponding Twitter project account are original tweets or retweets made from this account. It is relevant to mention that in one case, the Twitter account and Facebook page were linked to the website of the research group leading the project. In this case, we selected tweets and Facebook posts related to the project. For instance, in the case of the Twitter account, the research group created a specific hashtag to publish messages related to the project; therefore, we selected only the tweets published under this hashtag. In the analysis, we prioritized the analysis of the tweets and Facebook posts that received some type of interaction (likes, retweets or shares) because such interaction is a proxy for citizens’ interest. In doing so, we used the R program and NVivoto extract the data and proceed with the analysis. Once we obtained the data from Twitter and Facebook, we were able to have an overview of the information to be further analysed, as shown in Table 3 .

https://doi.org/10.1371/journal.pone.0203117.t003

We focused the second and third strategies on Twitter data. In both strategies, we extracted Twitter data directly from the Twitter Advanced Search tool, as the API connected to NVivo and the R program covers only a specific period of time limited to 7/9 days. Therefore, the use of the Twitter Advanced Search tool made it possible to obtain historic data without a period limitation. We downloaded the results in PDF and then uploaded them to NVivo.

Strategy 2. To use the project acronym combined with other keywords, such as FP7 or EU. This strategy made it possible to obtain tweets mentioning the project. Table 4 presents the number of tweets obtained with this strategy.

https://doi.org/10.1371/journal.pone.0203117.t004

Strategy 3. To use searchable research results of projects to obtain Twitter data. We defined a list of research results, one for each project, and converted them into keywords. We selected one searchable keyword for each project from its website or other relevant sources, for instance, the brief presentations prepared by the European Commission and published in CORDIS. Once we had the searchable research results, we used the Twitter Advanced Search tool to obtain tweets, as presented in Table 5 .

https://doi.org/10.1371/journal.pone.0203117.t005

The sum of the data obtained from these three strategies allowed us to obtain a total of 3,425 tweets and 1,925 posts on public Facebook pages. Table 6 presents a summary of the results.

https://doi.org/10.1371/journal.pone.0203117.t006

We imported the data obtained from the three search strategies into NVivo to analyse. Next, we select tweets and Facebook posts providing linkages with quantitative or qualitative evidence of social impact, and we complied with the terms of service for the social media from which the data were collected. By quantitative and qualitative evidence, we mean data or information that shows how the implementation of research results has led to improvements towards the fulfilment of the objectives defined in the EU2020 strategy of the European Commission or other official targets. For instance, in the case of quantitative evidence, we searched tweets and Facebook posts providing linkages with quantitative information about improvements obtained through the implementation of the research results of the project. In relation to qualitative evidence, for example, we searched for testimonies that show a positive evaluation of the improvement due to the implementation of research results. In relation to this step, it is important to highlight that social media users are intermediaries making visible evidence of social impact. Users often share evidence, sometimes sharing a link to an external resource (e.g., a video, an official report, a scientific article, news published on media). We identified evidence of social impact in these sources.

Data analysis

quantitative research topics about social media

γ i is the total number of messages obtained about project i with evidence of social impact on social media platforms (Twitter, Facebook, Instagram, etc.);

T i is the total number of messages from project i on social media platforms (Twitter, Facebook, Instagram, etc.); and

n is the number of projects selected.

Analytical categories and codebook

The researchers who carried out the analysis of the social media data collected are specialists in the social impact of research and research on social media. Before conducting the full analysis, two aspects were guaranteed. First, how to identify evidence of social impact relating to the targets defined by the EU2020 strategy or to specific goals defined by the call addressed was clarified. Second, we held a pilot to test the methodology with one research project that we know has led to considerable social impact, which allowed us to clarify whether or not it was possible to detect evidence of social impact shared in social media. Once the pilot showed positive results, the next step was to extend the analysis to another set of projects and finally to the whole sample. The construction of the analytical categories was defined a priori, revised accordingly and lastly applied to the full sample.

Different observations should be made. First, in this previous analysis, we found that the tweets and Facebook users play a key role as “intermediaries,” serving as bridges between the larger public and the evidence of social impact. Social media users usually share a quote or paragraph introducing evidence of social impact and/or link to an external resource, for instance, a video, official report, scientific article, news story published on media, etc., where evidence of the social impact is available. This fact has implications for our study, as our unit of analysis is all the information included in the tweets or Facebook posts. This means that our analysis reaches the external resources linked to find evidence of social impact, and for this reason, we defined tweets or Facebook posts providing linkages with information about social impact.

Second, the other important aspect is the analysis of the users’ profile descriptions, which requires much more development in future research given the existing limitations. For instance, some profiles are users’ restricted due to privacy reasons, so the information is not available; other accounts have only the name of the user with no description of their profile available. Therefore, we gave priority to the identification of evidence of social impact including whether a post obtained interaction (retweets, likes or shares) or was published on accounts other than that of the research project itself. In the case of the profile analysis, we added only an exploratory preliminary result because this requires further development. Considering all these previous details, the codebook (see Table 7 ) that we present as follows is a result of this previous research.

https://doi.org/10.1371/journal.pone.0203117.t007

How to analyse Twitter and Facebook data

To illustrate how we analysed data from Twitter and Facebook, we provide one example of each type of evidence of social impact defined, considering both real and potential social impact, with the type of interaction obtained and the profiles of those who have interacted.

QUANESISM. Tweet by ZeroHunger Challenge @ZeroHunger published on 3 May 2016. Text: How re-using food waste for animal feed cuts carbon emissions.-NOSHAN project hubs.ly/H02SmrP0. 7 retweets and 5 likes.

The unit of analysis is all the content of the tweet, including the external link. If we limited our analysis to the tweet itself, it would not be evidence. Examining the external link is necessary to find whether there is evidence of social impact. The aim of this project was to investigate the process and technologies needed to use food waste for feed production at low cost, with low energy consumption and with a maximal evaluation of the starting wastes. This tweet provides a link to news published in the PHYS.org portal [ 25 ], which specializes in science news. The news story includes an interview with the main researcher that provides the following quotation with quantitative evidence:

'Our results demonstrated that with a NOSHAN 10 percent mix diet, for every kilogram of broiler chicken feed, carbon dioxide emissions were reduced by 0.3 kg compared to a non-food waste diet,' explains Montse Jorba, NOSHAN project coordinator. 'If 1 percent of total chicken broiler feed in Europe was switched to the 10 percent NOSHAN mix diet, the total amount of CO2 emissions avoided would be 0.62 million tons each year.'[ 25 ]

This quantitative evidence “a NOSHAN 10 percent mix diet, for every kilogram of broiler chicken feed, carbon dioxide emissions carbon dioxide emissions were reduced by 0.3 kg to a non-food waste diet” is linked directly with the Europe 2020 target of Climate Change & Energy, specifically with the target of reducing greenhouse gas emissions by 20% compared to the levels in 1990 [ 8 ]. The illustrative extrapolation the coordinator mentioned in the news is also an example of quantitative evidence, although is an extrapolation based on the specific research result.

This tweet was captured by the Acronym search strategy. It is a message tweeted by an account that is not related to the research project. The twitter account is that of the Zero Hunger Challenge movement, which supports the goals of the UN. The interaction obtained is 7 retweets and 5 likes. Regarding the profiles of those who retweeted and clicked “like”, there were activists, a journalist, an eco-friendly citizen, a global news service, restricted profiles (no information is available on those who have retweeted) and one account with no information in its profile.

The following example illustrates the analysis of QUALESISM: Tweet by @eurofitFP7 published on4 October 2016. Text: See our great new EuroFIT video on youtube! https://t.co/TocQwMiW3c 9 retweets and 5 likes.

The aim of this project is to improve health through the implementation of two novel technologies to achieve a healthier lifestyle. The tweet provides a link to a video on YouTube on the project’s results. In this video, we found qualitative evidence from people who tested the EuroFit programme; there are quotes from men who said that they have experienced improved health results using this method and that they are more aware of how to manage their health:

One end-user said: I have really amazing results from the start, because I managed to change a lot of things in my life. And other one: I was more conscious of what I ate, I was more conscious of taking more steps throughout the day and also standing up a little more. [ 26 ]

The research applies the well researched scientific evidence to the management of health issues in daily life. The video presents the research but also includes a section where end-users talk about the health improvements they experienced. The quotes extracted are some examples of the testimonies collected. All agree that they have improved their health and learned healthy habits for their daily lives. These are examples of qualitative evidence linked with the target of the call HEALTH.2013.3.3–1—Social innovation for health promotion [ 27 ] that has the objectives of reducing sedentary habits in the population and promoting healthy habits. This research contributes to this target, as we see in the video testimonies. Regarding the interaction obtained, this tweet achieved 9 retweets and 5 likes. In this case, the profiles of the interacting citizens show involvement in sport issues, including sport trainers, sport enthusiasts and some researchers.

To summarize the analysis, in Table 8 below, we provide a summary with examples illustrating the evidence found.

https://doi.org/10.1371/journal.pone.0203117.t008

Quantitative evidence of social impact in social media

There is a greater presence of tweets/Fb posts with quantitative evidence (14) than with qualitative evidence (9) in the total number of tweets/Fb posts identified with evidence of social impact. Most of the tweets/Fb posts with quantitative evidence of social impact are from scientific articles published in peer-reviewed international journals and show potential social impact. In Table 8 , we introduce 3 examples of this type of tweets/Fb posts with quantitative evidence:

The first tweet with quantitative social impact selected is from project 7. The aim of this project was to provide high-quality scientific evidence for preventing vitamin D deficiency in European citizens. The tweet highlighted the main contribution of the published study, that is, “Weekly consumption of 7 vitamin D-enhanced eggs has an important impact on winter vitamin D status in adults” [ 28 ]. The quantitative evidence shared in social media was extracted from a news publication in a blog on health news. This blog collects scientific articles of research results. In this case, the blog disseminated the research result focused on how vitamin D-enhanced eggs improve vitamin D deficiency in wintertime, with the published results obtained by the research team of the project selected. The quantitative evidence illustrates that the group of adults who consumed vitamin D-enhanced eggs did not suffer from vitamin D deficiency, as opposed to the control group, which showed a significant decrease in vitamin D over the winter. The specific evidence is the following extracted from the article [ 28 ]:

With the use of a within-group analysis, it was shown that, although serum 25(OH) D in the control group significantly decreased over winter (mean ± SD: -6.4 ± 6.7 nmol/L; P = 0.001), there was no change in the 2 groups who consumed vitamin D-enhanced eggs (P>0.1 for both. (p. 629)

This evidence contributes to achievement of the target defined in the call addressed that is KBBE.2013.2.2–03—Food-based solutions for the eradication of vitamin D deficiency and health promotion throughout the life cycle [ 29 ]. The quantitative evidence shows how the consumption of vitamin D-enhanced eggs reduces vitamin D deficiency.

The second example of this table corresponds to the example of quantitative evidence of social impact provided in the previous section.

The third example is a Facebook post from project 3 that is also tweeted. Therefore, this evidence was published in both social media sources analysed. The aim of this project was to measure a range of chemical and physical environmental hazards in food, consumer products, water, air, noise, and the built environment in the pre- and postnatal early-life periods. This Facebook post and tweet links directly to a scientific article [ 30 ] that shows the precision of the spectroscopic platform:

Using 1H NMR spectroscopy we characterized short-term variability in urinary metabolites measured from 20 children aged 8–9 years old. Daily spot morning, night-time and pooled (50:50 morning and night-time) urine samples across six days (18 samples per child) were analysed, and 44 metabolites quantified. Intraclass correlation coefficients (ICC) and mixed effect models were applied to assess the reproducibility and biological variance of metabolic phenotypes. Excellent analytical reproducibility and precision was demonstrated for the 1H NMR spectroscopic platform (median CV 7.2%) . (p.1)

This evidence is linked to the target defined in the call “ENV.2012.6.4–3—Integrating environmental and health data to advance knowledge of the role of environment in human health and well-being in support of a European exposome initiative” [ 31 ]. The evidence provided shows how the project’s results have contributed to building technology for improving the data collection to advance in the knowledge of the role of the environment in human health, especially in early life. The interaction obtained is one retweet from a citizen from Nigeria interested in health issues, according to the information available in his profile.

Qualitative evidence of social impact in social media

We found qualitative evidence of the social impact of different projects, as shown in Table 9 . Similarly to the quantitative evidence, the qualitative cases also demonstrate potential social impact. The three examples provided have in common that they are tweets or Facebook posts that link to videos where the end users of the research project explain their improvements once they have implemented the research results.

https://doi.org/10.1371/journal.pone.0203117.t009

The first tweet with qualitative evidence selected is from project 4. The aim of this project is to produce a system that helps in the prevention of obesity and eating disorders, targeting young people and adults [ 32 ]. The twitter account that published this tweet is that of the Future and Emerging Technologies Programme of the European Commission, and a link to a Euronews video is provided. This video shows how the patients using the technology developed in the research achieved control of their eating disorders, through the testimonies of patients commenting on the positive results they have obtained. These testimonies are included in the news article that complements the video. An example of these testimonies is as follows:

Pierre Vial has lost 43 kilos over the past nine and a half months. He and other patients at the eating disorder clinic explain the effects obesity and anorexia have had on their lives. Another patient, Karin Borell, still has some months to go at the clinic but, after decades of battling anorexia, is beginning to be able to visualise life without the illness: “On a good day I see myself living a normal life without an eating disorder, without problems with food. That’s really all I wish right now”.[ 32 ]

This qualitative evidence shows how the research results contribute to the achievement of the target goals of the call addressed:“ICT-2013.5.1—Personalised health, active ageing, and independent living”. [ 33 ] In this case, the results are robust, particularly for people suffering chronic diseases and desiring to improve their health; people who have applied the research findings are improving their eating disorders and better managing their health. The value of this evidence is the inclusion of the patients’ voices stating the impact of the research results on their health.

The second example is a Facebook post from project 9, which provides a link to a Euronews video. The aim of this project is to bring some tools from the lab to the farm in order to guarantee a better management of the farm and animal welfare. In this video [ 34 ], there are quotes from farmers using the new system developed through the research results of the project. These quotes show how use of the new system is improving the management of the farm and the health of the animals; some examples are provided:

Cameras and microphones help me detect in real time when the animals are stressed for whatever reason,” explained farmer Twan Colberts. “So I can find solutions faster and in more efficient ways, without me being constantly here, checking each animal.”

This evidence shows how the research results contribute to addressing the objectives specified in the call “KBBE.2012.1.1–02—Animal and farm-centric approach to precision livestock farming in Europe” [ 29 ], particularly, to improve the precision of livestock farming in Europe. The interaction obtained is composed of6 likes and 1 share. The profiles are diverse, but some of them do not disclose personal information; others have not added a profile description, and only their name and photo are available.

Interrater reliability (kappa)

The analysis of tweets and Facebook posts providing linkages with information about social impact was conducted following a content analysis method in which reliability was based on a peer review process. This sample is composed of 3,425 tweets and 1,925 Fb/posts. Each tweet and Facebook post was analysed to identify whether or not it contains evidence of social impact. Each researcher has the codebook a priori. We used interrater reliability in examining the agreement between the two raters on the assignment of the categories defined through Cohen’s kappa. We used SPSS to calculate this coefficient. We exported an excel sheet with the sample coded by the two researchers being 1 (is evidence of social impact, either potential or real) and 0 (is not evidence of social impact) to SPSS. The cases where agreement was not achieved were not considered as containing evidence of social impact. The result obtained is 0.979; considering the interpretation of this number according to Landis & Koch [ 35 ], our level of agreement is almost perfect, and thus, our analysis is reliable. To sum up the data analysis, the description of the steps followed is explained:

Step 1. Data analysis I. We included all data collected in an excel sheet to proceed with the analysis. Prior to the analysis, researchers read the codebook to keep in mind the information that should be identified.

Step 2. Each researcher involved reviewed case by case the tweets and Facebook posts to identify whether they provide links with evidence of social impact or not. If the researcher considers there to be evidence of social impact, he or she introduces the value of 1into the column, and if not, the value of 0.

Step 3. Once all the researchers have finished this step, the next step is to export the excel sheet to SPSS to extract the kappa coefficient.

Step 4. Data Analysis II. The following step was to analyse case by case the tweets and Facebook posts identified as providing linkages with information of social impact and classify them as quantitative or qualitative evidence of social impact.

Step 5. The interaction received was analysed because this determines to which extent this evidence of social impact has captured the attention of citizens (in the form of how many likes, shares, or retweets the post has).

Step 6. Finally, if available, the profile descriptions of the citizens interacting through retweeting or sharing the Facebook post were considered.

Step 7. SICOR was calculated. It could be applied to the complete sample (all data projects) or to each project, as we will see in the next section.

The total number of tweets and Fb/posts collected from the 10 projects is 5,350. After the content analysis, we identified 23 tweets and Facebook posts providing linkages to information about social impact. To respond to the research question, which considered whether there is evidence of social impact shared by citizens in social media, the answer was affirmative, although the coverage ratio is low. Both Twitter and Facebook users retweeted or shared evidence of social impact, and therefore, these two social media networks are valid sources for expanding knowledge on the assessment of social impact. Table 10 shows the social impact coverage ratio in relation to the total number of messages analysed.

https://doi.org/10.1371/journal.pone.0203117.t010

The analysis of each of the projects selected revealed some results to consider. Of the 10 projects, 7 had evidence, but those projects did not necessarily have more Tweets and Facebook posts. In fact, some projects with fewer than 70 tweets and 50 Facebook posts have more evidence of social impact than other projects with more than 400 tweets and 400 Facebook posts. This result indicates that the number of tweets and Facebook posts does not determine the existence of evidence of social impact in social media. For example, project 2 has 403 tweets and 423 Facebooks posts, but it has no evidence of social impact on social media. In contrast, project 9 has 62 tweets, 43 Facebook posts, and 2 pieces of evidence of social impact in social media, as shown in Table 11 .

https://doi.org/10.1371/journal.pone.0203117.t011

The ratio of tweets/Fb posts to evidence is 0.43%, and it differs depending on the project, as shown below in Table 12 . There is one project (P7) with a ratio of 4.98%, which is a social impact coverage ratio higher than that of the other projects. Next, a group of projects (P3, P9, P10) has a social impact coverage ratio between 1.41% and 2,99%.The next slot has three projects (P1, P4, P5), with a ratio between 0.13% and 0.46%. Finally, there are three projects (P2, P6, P8) without any tweets/Fb posts evidence of social impact.

https://doi.org/10.1371/journal.pone.0203117.t012

Considering the three strategies for obtaining data, each is related differently to the evidence of social impact. In terms of the social impact coverage ratio, as shown in Table 13 , the most successful strategy is number 3 (searchable research results), as it has a relation of 17.86%, which is much higher than the ratios for the other 2 strategies. The second strategy (acronym search) is more effective than the first (profile accounts),with 1.77% for the former as opposed to 0.27% for the latter.

https://doi.org/10.1371/journal.pone.0203117.t013

Once tweets and Facebook posts providing linkages with information about social impact(ESISM)were identified, we classified them in terms of quantitative (QUANESISM) or qualitative evidence (QUALESISM)to determine which type of evidence was shared in social media. Table 14 indicates the amount of quantitative and qualitative evidence identified for each search strategy.

https://doi.org/10.1371/journal.pone.0203117.t014

First, the results obtained indicated that the SISM methodology aids in calculating the social impact coverage ratio of the research projects selected and evaluating whether the social impact of the corresponding research is shared by citizens in social media. The social impact coverage ratio applied to the sample selected is low, but when we analyse the SICOR of each project separately, we can observe that some projects have a higher social impact coverage ratio than others. Complementary to altmetrics measuring the extent to which research results reach out society, the SICOR considers the question whether this process includes evidence of potential or real social impact. In this sense, the overall methodology of SISM contributes to advancement in the evaluation of the social impact of research by providing a more precise approach to what we are evaluating.

This contribution complements current evaluation methodologies of social impact that consider which improvements are shared by citizens in social media. Exploring the results in more depth, it is relevant to highlight that of the ten projects selected, there is one research project with a social impact coverage ratio higher than those of the others, which include projects without any tweets or Facebook posts with evidence of social impact. This project has a higher ratio of evidence than the others because evidence of its social impact is shared more than is that of other projects. This also means that the researchers produced evidence of social impact and shared it during the project. Another relevant result is that the quantity of tweets and Fb/posts collected did not determine the number of tweets and Fb/posts found with evidence of social impact. Moreover, the analysis of the research projects selected showed that there are projects with less social media interaction but with more tweets and Fb/posts containing evidence of social media impact. Thus, the number of tweets and Fb/posts with evidence of social impact is not determined by the number of publication messages collected; it is determined by the type of messages published and shared, that is, whether they contain evidence of social impact or not.

The second main finding is related to the effectiveness of the search strategies defined. Related to the strategies carried out under this methodology, one of the results found is that the most effective search strategy is the searchable research results, which reveals a higher percentage of evidence of social impact than the own account and acronym search strategies. However, the use of these three search strategies is highly recommended because the combination of all of them makes it possible to identify more tweets and Facebook posts with evidence of social impact.

Another result is related to the type of evidence of social impact found. There is both quantitative and qualitative evidence. Both types are useful for understanding the type of social impact achieved by the corresponding research project. In this sense, quantitative evidence allows us to understand the improvements obtained by the implementation of the research results and capture their impact. In contrast, qualitative evidence allows us to deeply understand how the resultant improvements obtained from the implementation of the research results are evaluated by the end users by capturing their corresponding direct quotes. The social impact includes the identification of both real and potential social impact.

Conclusions

After discussing the main results obtained, we conclude with the following points. Our study indicates that there is incipient evidence of social impact, both potential and real, in social media. This demonstrates that researchers from different fields, in the present case involved in medical research, public health, animal welfare and genomics, are sharing the improvements generated by their research and opening up new venues for citizens to interact with their work. This would imply that scientists are promoting not only the dissemination of their research results but also the evidence on how their results may lead to the improvement of societies. Considering the increasing relevance and presence of the dissemination of research, the results indicate that scientists still need to include in their dissemination and communication strategies the aim of sharing the social impact of their results. This implies the publication of concrete qualitative or quantitative evidence of the social impact obtained. Because of the inclusion of this strategy, citizens will pay more attention to the content published in social media because they are interested in knowing how science can contribute to improving their living conditions and in accessing crucial information. Sharing social impact in social media facilitates access to citizens of different ages, genders, cultural backgrounds and education levels. However, what is most relevant for our argument here is how citizens should also be able to participate in the evaluation of the social impact of research, with social media a great source to reinforce this democratization process. This contributes not only to greatly improving the social impact assessment, as in addition to experts, policy makers and scientific publications, citizens through social media contribute to making this assessment much more accurate. Thus, citizens’ contribution to the dissemination of evidence of the social impact of research yields access to more diverse sectors of society and information that might be unknown by the research or political community. Two future steps are opened here. On the one hand, it is necessary to further examine the profiles of users who interact with this evidence of social impact considering the limitations of the privacy and availability of profile information. A second future task is to advance in the articulation of the role played by citizens’ participation in social impact assessment, as citizens can contribute to current worldwide efforts by shedding new light on this process of social impact assessment and contributing to making science more relevant and useful for the most urgent and poignant social needs.

Supporting information

S1 file. interrater reliability (kappa) result..

This file contains the SPSS file with the result of the calculation of Cohen’s Kappa regards the interrater reliability. The word document exported with the obtained result is also included.

https://doi.org/10.1371/journal.pone.0203117.s001

S2 File. Data collected and SICOR calculation.

This excel contains four sheets, the first one titled “data collected” contains the number of tweets and Facebook posts collected through the three defined search strategies; the second sheet titled “sample” contains the sample classified by project indicating the ID of the message or code assigned, the type of message (tweet or Facebook post) and the codification done by researchers being 1 (is evidence of social impact, either potential or real) and 0 (is not evidence of social impact); the third sheet titled “evidence found” contains the number of type of evidences of social impact founded by project (ESISM-QUANESIM or ESISM-QUALESIM), search strategy and type of message (tweet or Facebook posts); and the last sheet titled “SICOR” contains the Social Impact Coverage Ratio calculation by projects in one table and type of search strategy done in another one.

https://doi.org/10.1371/journal.pone.0203117.s002

Acknowledgments

The research leading to these results received funding from the 7 th Framework Programme of the European Commission under Grant Agreement n° 613202. The extraction of available data using the list of searchable keywords on Twitter and Facebook followed the ethical guidelines for social media research supported by the Economic and Social Research Council (UK) [ 36 ] and the University of Aberdeen [ 37 ]. Furthermore, the research results have already been published and made public, and hence, there are no ethical issues.

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14 Best Social Media Research Tools you should be looking for in 2022

Johnny Tong

Apr 22, 2022

14 Best Social Media Research Tools you should be looking for in 2022

Table of Contents

What Is Social Media Research?

Top 14 social media research tools of 2022.

1. Surveybot
2. Sprout Social
3. Hootsuite
4. Talkwalker
5. Social Mention
6. Twitter Analytics
7. Facebook Analytics
8. Youtube Studio
9. Synthesio
10. Mentionlytics
11. Tweepsmap
13. Nexalogy
14. Reddit Search

A Few Social Media Research Best Practices

How to select the ideal social media research tool.

Marketers may want to know how they can improve their product, what kind of content can go viral, what kind of audience to target, etc.
In contrast, researchers will want to find public opinions on some pressing matters concerning their research.

1. Surveybot

Intuitive UI
Robust Analytics
Comparatively young as a platform
Constant updating with demanded features

2. Sprout Social

User friendly
Intuitive Reports
Steep pricing
Limited analytics

3. Hootsuite

Reliable ‘Free’ Plan
Ease of Use
Multi-channel dashboard
Slow reporting
Limitations in integrating with Instagram

4. Talkwalker

Excellent customer support
Easy to use
Multiple dashboards
Dull Interface

5. Social Mention

Real-time data of campaigns
Ease of use
Limited filtering capabilities

6 . Twitter Analytics

Easy to set up
Inefficient customer support system
Insecure login capabilities

7. Facebook Analytics

Custom dashboard
Strong visualization capabilities
Sophisticated Interface
Limited integration capabilities

8. Youtube Studio

Feature-rich
Reliable analytics
Mediocre management of comments
Only applicable if your content is published on YouTube

9. Synthesio

Responsive customer support
Limited customization in reports

10. Mentionlytics

Easy to use and set-up
Great Customer service
Limitations in branding reports
Limited dashboard customization

11. Tweepsmap

Deep analytical capabilities
Excellent customer service
Lacks the ability to create an integrated campaign
All the capabilities are limited to Twitter

12. Pulsar

Dedicated features to analyze the competition
Excellent quality of support
Simple learning curve
Limited data from social media platforms like Linkedin

13. Nexalogy

Powerful API
Expensive pricing plans
Limited customer support capabilities
Mobile app not available

14. Reddit Search

Totally free
Minimalist UI
Basic tool limited to Reddit
Define your goal : What are you researching for ⁠— product launch, defining buyer persona, getting insights on target customers, ideas for new products? What exactly? Define!
Define your target group: If you are conducting qualitative research (and to some extent even quantitative research), it’s important to know whose opinion is worth taking. This will help you get valuable data and help you plan and decide better (about the tools needed, the social media channels to use, the keywords to pursue, etc.).
Numbers aren’t everything : The best answers from quantitative social media research may not be the best answers. For example, what attracts the most likes doesn’t always attract the most conversions (your goal). So look beyond the numbers ⁠— probably do something like sentiment analysis , opinion mining, etc., to not heavily rely on numbers.
Clean your data : Even if you have defined the goal and the target group to the T, you will receive irrelevant, inaccurate, and/or incomplete data. Therefore, it’s crucial to filter out such data to reach the best conclusion(s).
Strategize based on conclusions : Especially for businesses that engage in social media research, your job isn’t done once you conclude the research. In fact, the actual job starts after. Do something with the research. Conducted the research for product ideas? Make the product!

Chief Human @ Surveybot

Analyzing social media data: A mixed-methods framework combining computational and qualitative text analysis

Published: 02 April 2019
Volume 51 , pages 1766–1781, ( 2019 )

Cite this article

Matthew Andreotta 1 , 2 ,
Robertus Nugroho 2 , 3 ,
Mark J. Hurlstone 1 ,
Fabio Boschetti 4 ,
Simon Farrell 1 ,
Iain Walker 5 &
Cecile Paris 2

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To qualitative researchers, social media offers a novel opportunity to harvest a massive and diverse range of content without the need for intrusive or intensive data collection procedures. However, performing a qualitative analysis across a massive social media data set is cumbersome and impractical. Instead, researchers often extract a subset of content to analyze, but a framework to facilitate this process is currently lacking. We present a four-phased framework for improving this extraction process, which blends the capacities of data science techniques to compress large data sets into smaller spaces, with the capabilities of qualitative analysis to address research questions. We demonstrate this framework by investigating the topics of Australian Twitter commentary on climate change, using quantitative (non-negative matrix inter-joint factorization; topic alignment) and qualitative (thematic analysis) techniques. Our approach is useful for researchers seeking to perform qualitative analyses of social media, or researchers wanting to supplement their quantitative work with a qualitative analysis of broader social context and meaning.

Text Mining and Big Textual Data: Relevant Statistical Models

Word Association Thematic Analysis: Insight Discovery from the Social Web

Comparing qualitative and quantitative text analysis methods in combination with document-based social network analysis to understand policy networks

Explore related subjects.

Artificial Intelligence

Avoid common mistakes on your manuscript.

Introduction

Social scientists use qualitative modes of inquiry to explore the detailed descriptions of the world that people see and experience (Pistrang & Barker, 2012 ). To collect the voices of people, researchers can elicit textual descriptions of the world through interview or survey methodologies. However, with the popularity of the Internet and social media technologies, new avenues for data collection are possible. Social media platforms allow users to create content (e.g., Weinberg & Pehlivan, 2011 ), and interact with other users (e.g., Correa, Hinsley, & de Zùñiga, 2011 ; Kietzmann, Hermkens, McCarthy, & Silvestre, 2010 ), in settings where “Anyone can say Anything about Any topic” ( AAA slogan , Allemang & Hendler, 2011 , pg. 6). Combined with the high rate of content production, social media platforms can offer researchers massive and diverse dynamic data sets (Yin & Kaynak, 2015 ; Gudivada et al., 2015 ). With technologies increasingly capable of harvesting, storing, processing, and analyzing this data, researchers can now explore data sets that would be infeasible to collect through more traditional qualitative methods.

Many social media platforms can be considered as textual corpora, willingly and spontaneously authored by millions of users. Researchers can compile a corpus using automated tools and conduct qualitative inquiries of content or focused analyses on specific users (Marwick, 2014 ). In this paper, we outline some of the opportunities and challenges of applying qualitative textual analyses to the big data of social media. Specifically, we present a conceptual and pragmatic justification for combining qualitative textual analyses with data science text-mining tools. This process allows us to both embrace and cope with the volume and diversity of commentary over social media. We then demonstrate this approach in a case study investigating Australian commentary on climate change, using content from the social media platform: Twitter.

Opportunities and challenges for qualitative researchers using social media data

Through social media, qualitative researchers gain access to a massive and diverse range of individuals, and the content they generate. Researchers can identify voices which may not be otherwise heard through more traditional approaches, such as semi-structured interviews and Internet surveys with open-ended questions. This can be done through diagnostic queries to capture the activity of specific peoples, places, events, times, or topics. Diagnostic queries may specify geotagged content, the time of content creation, textual content of user activity, and the online profile of users. For example, Freelon et al., ( 2018 ) identified the Twitter activity of three separate communities (‘Black Twitter’, ‘Asian-American Twitter’, ‘Feminist Twitter’) through the use of hashtags Footnote 1 in tweets from 2015 to 2016. A similar process can be used to capture specific events or moments (Procter et al., 2013 ; Denef et al., 2013 ), places (Lewis et al., 2013 ), and specific topics (Hoppe, 2009 ; Sharma et al., 2017 ).

Collecting social media data may be more scalable than traditional approaches. Once equipped with the resources to access and process data, researchers can potentially scale data harvesting without expending a great deal of resources. This differs from interviews and surveys, where collecting data can require an effortful and time-consuming contribution from participants and researchers.

Social media analyses may also be more ecologically valid than traditional approaches. Unlike approaches where responses from participants are elicited in artificial social contexts (e.g., Internet surveys, laboratory-based interviews), social media data emerges from real-world social environments encompassing a large and diverse range of people, without any prompting from researchers. Thus, in comparison with traditional methodologies (Onwuegbuzie and Leech, 2007 ; Lietz & Zayas, 2010 ; McKechnie, 2008 ), participant behavior is relatively unconstrained if not entirely unconstrained, by the behaviors of researchers.

These opportunities also come up with challenges, because of the following attributes (Parker et al., 2011 ). Firstly, social media can be interactive : its content involves the interactions of users with other users (e.g., conversations), or even external websites (e.g., links to news websites). The ill-defined boundaries of user interaction have implications for determining the units of analysis of qualitative study. For example, conversations can be lengthy, with multiple users, without a clear structure or end-point. Interactivity thus blurs the boundaries between users, their content, and external content (Herring, 2009 ; Parker et al., 2011 ). Secondly, content can be ephemeral and dynamic . The users and content of their postings are transient (Parker et al., 2011 ; Boyd & Crawford, 2012 ; Weinberg & Pehlivan, 2011 ). This feature arises from the diversity of users, the dynamic socio-cultural context surrounding platform use, and the freedom users have to create, distribute, display, and dispose of their content (Marwick & Boyd, 2011 ). Lastly, social media content is massive in volume . The accumulated postings of users can lead to a large amount of data, and due to the diverse and dynamic content, postings may be largely unrelated and accumulate over a short period of time. Researchers hoping to harness the opportunities of social media data sets must therefore develop strategies for coping with these challenges.

A framework integrating computational and qualitative text analyses

Our framework—a mixed-method approach blending the capabilities of data science techniques with the capacities of qualitative analysis—is shown in Fig. 1 . We overcome the challenges of social media data by automating some aspects of the data collection and consolidation, so that the qualitative researcher is left with a manageable volume of data to synthesize and interpret. Broadly, our framework consists of the following four phases: (1) harvest social media data and compile a corpus, (2) use data science techniques to compress the corpus along a dimension of relevance, (3) extract a subset of data from the most relevant spaces of the corpus, and (4) perform a qualitative analysis on this subset of data.

Schematic overview of the four-phased framework

Phase 1: Harvest social media data and compile a corpus

Researchers can use automated tools to query records of social media data, extract this data, and compile it into a corpus. Researchers may query for content posted in a particular time frame (Procter et al., 2013 ), content containing specified terms (Sharma et al., 2017 ), content posted by users meeting particular characteristics (Denef et al., 2013 ; Lewis et al., 2013 ), and content pertaining to a specified location (Hoppe, 2009 ).

Phase 2: Use data science techniques to compress the corpus along a dimension of relevance

Although researchers may be interested in examining the entire data set, it is often more practical to focus on a subsample of data (McKenna et al., 2017 ). Specifically, we advocate dividing the corpus along a dimension of relevance, and sampling from spaces that are more likely to be useful for addressing the research questions under consideration. By relevance, we refer to an attribute of content that is both useful for addressing the research questions and usable for the planned qualitative analysis.

To organize the corpus along a dimension of relevance , researchers can use automated, computational algorithms. This process provides both formal and informal advantages for the subsequent qualitative analysis. Formally, algorithms can assist researchers in privileging an aspect of the corpus most relevant for the current inquiry. For example, topic modeling clusters massive content into semantic topics—a process that would be infeasible using human coders alone. A plethora of techniques exist for separating social media corpora on the basis of useful aspects, such as sentiment (e.g., Agarwal, Xie, Vovsha, Rambow, & Passonneau, 2010 ; Paris, Christensen, Batterham, & O’Dea, 2015 ; Pak & Paroubek, 2011 ) and influence (Weng et al., 2010 ).

Algorithms also produce an informal advantage for qualitative analysis. As mentioned, it is often infeasible for analysts to explore large data sets using qualitative techniques. Computational models of content can allow researchers to consider meaning at a corpus-level when interpreting individual datum or relationships between a subset of data. For example, in an inspection of 2.6 million tweets, Procter et al., ( 2013 ) used the output of an information flow analysis to derive rudimentary codes for inspecting individual tweets. Thus, algorithmic output can form a meaningful scaffold for qualitative analysis by providing analysts with summaries of potentially disjunct and multifaceted data (due to interactive, ephemeral, dynamic attributes of social media).

Phase 3: Extract a subset of data from the most relevant spaces of the corpus

Once the corpus is organized on the basis of relevance, researchers can extract data most relevant for answering their research questions. Researchers can extract a manageable amount of content to qualitatively analyze. For example, if the most relevant space of the corpus is too large for qualitative analysis, the researcher may choose to randomly sample from that space. If the most relevant space is small, the researcher may revisit Phase 2 and adopt a more lenient criteria of relevance.

Phase 4: Perform a qualitative analysis on this subset of data

The final phase involves performing the qualitative analysis to address the research question. As discussed above, researchers may draw on the computational models as a preliminary guide to the data.

Contextualizing the framework within previous qualitative social media studies

The proposed framework generalizes a number of previous approaches (Collins and Nerlich, 2015 ; McKenna et al., 2017 ) and individual studies (e.g., Lewis et al., 2013 ; Newman, 2016 ), in particular that of Marwick ( 2014 ). In Marwick’s general description of qualitative analysis of social media textual corpora, researchers: (1) harvest and compile a corpus, (2) extract a subset of the corpus, and (3) perform a qualitative analysis on the subset. As shown in Fig. 1 , our framework differs in that we introduce formal considerations of relevance, and the use of quantitative techniques to inform the extraction of a subset of data. Although researchers sometimes identify a subset of data most relevant to answering their research question, they seldom deploy data science techniques to identify it. Instead, researchers typically depend on more crude measures to isolate relevant data. For example, researchers have used the number of repostings of user content to quantify influence and recognition (e.g., Newman, 2016 ).

The steps in the framework may not be obvious without a concrete example. Next, we demonstrate our framework by applying it to Australian commentary regarding climate change on Twitter.

Application Example: Australian Commentary regarding Climate Change on Twitter

Social media platform of interest.

We chose to explore user commentary of climate change over Twitter. Twitter activity contains information about: the textual content generated by users (i.e., content of tweets), interactions between users, and the time of content creation (Veltri and Atanasova, 2017 ). This allows us to examine the content of user communication, taking into account the temporal and social contexts of their behavior. Twitter data is relatively easy for researchers to access. Many tweets reside within a public domain, and are accessible through free and accessible APIs.

The characteristics of Twitter’s platform are also favorable for data analysis. An established literature describes computational techniques and considerations for interpreting Twitter data. We used the approaches and findings from other empirical investigations to inform our approach. For example, we drew on past literature to inform the process of identifying which tweets were related to climate change.

Public discussion on climate change

Climate change is one of the greatest challenges facing humanity (Schneider, 2011 ). Steps to prevent and mitigate the damaging consequences of climate change require changes on different political, societal, and individual levels (Lorenzoni & Pidgeon, 2006 ). Insights into public commentary can inform decision making and communication of climate policy and science.

Traditionally, public perceptions are investigated through survey designs and qualitative work (Lorenzoni & Pidgeon, 2006 ). Inquiries into social media allow researchers to explore a large and diverse range of climate change-related dialogue (Auer et al., 2014 ). Yet, existing inquiries of Twitter activity are few in number and typically constrained to specific events related to climate change, such as the release of the Fifth Assessment Report by the Intergovernmental Panel on Climate Change (Newman et al., 2010 ; O’Neill et al., 2015 ; Pearce, 2014 ) and the 2015 United Nations Climate Change Conference, held in Paris (Pathak et al., 2017 ).

When longer time scales are explored, most researchers rely heavily upon computational methods to derive topics of commentary. For example, Kirilenko and Stepchenkova ( 2014 ) examined the topics of climate change tweets posted in 2012, as indicated by the most prevalent hashtags. Although hashtags can mark the topics of tweets, it is a crude measure as tweets with no hashtags are omitted from analysis, and not all topics are indicated via hashtags (e.g., Nugroho, Yang, Zhao, Paris, & Nepal, 2017 ). In a more sophisticated approach, Veltri and Atanasova ( 2017 ) examined the co-occurrence of terms using hierarchical clustering techniques to map the semantic space of climate change tweet content from the year 2013. They identified four themes: (1) “calls for action and increasing awareness”, (2) “discussions about the consequences of climate change”, (3) “policy debate about climate change and energy”, and (4) “local events associated with climate change” (p. 729).

Our research builds on the existing literature in two ways. Firstly, we explore a new data set—Australian tweets over the year 2016. Secondly, in comparison to existing research of Twitter data spanning long time periods, we use qualitative techniques to provide a more nuanced understanding of the topics of climate change. By applying our mixed-methods framework, we address our research question: what are the common topics of Australian’s tweets about climate change?

Outline of approach

We employed our four-phased framework as shown in Fig. 2 . Firstly, we harvested climate change tweets posted in Australia in 2016 and compiled a corpus (phase 1). We then utilized a topic modeling technique (Nugroho et al., 2017 ) to organize the diverse content of the corpus into a number of topics. We were interested in topics which commonly appeared throughout the time period of data collection, and less interested in more transitory topics. To identify enduring topics, we used a topic alignment algorithm (Chuang et al., 2015 ) to group similar topics occurring repeatedly throughout 2016 (phase 2). This process allowed us to identify the topics most relevant to our research question. From each of these, we extracted a manageable subset of data (phase 3). We then performed a qualitative thematic analysis (see Braun & Clarke, 2006 ) on this subset of data to inductively derive themes and answer our research question (phase 4). Footnote 2

Flowchart of application of a four-phased framework for conducting qualitative analyses using data science techniques. We were most interested in topics that frequently occurred throughout the period of data collection. To identify these, we organized the corpus chronologically, and divided the corpus into batches of content. Using computational techniques (shown in blue ), we uncovered topics in each batch and identified similar topics which repeatedly occurred across batches. When identifying topics in each batch, we generated three alternative representations of topics (5, 10, and 20 topics in each batch, shown in yellow ). In stages highlighted in green , we determined the quality of these representations, ultimately selecting the five topics per batch solution

Phase 1: Compiling a corpus

To search Australian’s Twitter data, we used CSIRO’s Emergency Situation Awareness (ESA) platform (CSIRO, 2018 ). The platform was originally built to detect, track, and report on unexpected incidences related to crisis situations (e.g., fires, floods; see Cameron, Power, Robinson, & Yin 2012 ). To do so, the ESA platform harvests tweets based on a location search that covers most of Australia and New Zealand.

The ESA platform archives the harvested tweets, which may be used for other CSIRO research projects. From this archive, we retrieved tweets satisfying three criteria: (1) tweets must be associated with an Australian location, (2) tweets must be harvested from the year 2016, and (3) the content of tweets must be related to climate change. We tested the viability of different markers of climate change tweets used in previous empirical work (Jang & Hart, 2015 ; Newman, 2016 ; Holmberg & Hellsten, 2016 ; O’Neill et al., 2015 ; Pearce et al., 2014 ; Sisco et al., 2017 ; Swain, 2017 ; Williams et al., 2015 ) by informally inspecting the content of tweets matching each criteria. Ultimately, we employed five terms (or combinations of terms) reliably associated with climate change: (1) “climate” AND “change”; (2) “#climatechange”; (3) “#climate”; (4) “global” AND “warming”; and (5) “#globalwarming”. This yielded a corpus of 201,506 tweets.

Phase 2: Using data science techniques to compress the corpus along a dimension of relevance

The next step was to organize the collection of tweets into distinct topics. A topic is an abstract representation of semantically related words and concepts. Each tweet belongs to a topic, and each topic may be represented as a list of keywords (i.e., prominent words of tweets belonging to the topic).

A vast literature surrounds the computational derivation of topics within textual corpora, and specifically within Twitter corpora (Ramage et al., 2010 ; Nugroho et al., 2017 ; Fang et al., 2016a ; Chuang et al., 2014 ). Popular methods for deriving topics include: probabilistic latent semantic analysis (Hofmann, 1999 ), non-negative matrix factorization (Lee & Seung, 2000 ), and latent Dirichlet allocation (Blei et al., 2003 ). These approaches use patterns of co-occurrence of terms within documents to derive topics. They work best on long documents. Tweets, however, are short, and thus only a few unique terms may co-occur between tweets. Consequently, approaches which rely upon patterns of term co-occurrence suffer within the Twitter environment. Moreover, these approaches ignore valuable social and temporal information (Nugroho et al., 2017 ). For example, consider a tweet t 1 and its reply t 2 . The reply feature of Twitter allows users to react to tweets and enter conversations. Therefore, it is likely t 1 and t 2 are related in topic, by virtue of the reply interaction.

To address sparsity concerns, we adopt the non-negative matrix inter-joint factorization (NMijF) of Nugroho et al., ( 2017 ). This process uses both tweet content (i.e., the patterns of co-occurrence of terms amongst tweets) and socio-temporal relationship between tweets (i.e., similarities in the users mentioned in tweets, whether the tweet is a reply to another tweet, whether tweets are posted at a similar time) to derive topics (see Supplementary Material ). The NMijF method has been demonstrated to outperform other topic modeling techniques on Twitter data (Nugroho et al., 2017 ).

Dividing the corpus into batches

Deriving many topics across a data set of thousands of tweets is prohibitively expensive in computational terms. Therefore, we divided the corpus into smaller batches and derived the topics of each batch. To keep the temporal relationships amongst tweets (e.g., timestamps of the tweets) the batches were organized chronologically. The data was partitioned into 41 disjoint batches (40 batches of 5000 tweets; one batch of 1506 tweets).

Generating topical representations for each batch

Following standard topic modeling practice, we removed features from each tweet which may compromise the quality of the topic derivation process. These features include: emoticons, punctuation, terms with fewer than three characters, stop-words (for list of stop-words, see MySQL, 2018 ), and phrases used to harvest the data (e.g., “#climatechange”). Footnote 3 Following this, the terms remaining in tweets were stemmed using the Natural Language Toolkit for Python (Bird et al., 2009 ). All stemmed terms were then tokenized for processing.

The NMijF topic derivation process requires three parameters (see Supplementary Material for more details). We set two of these parameters to the recommendations of Nugroho et al., ( 2017 ), based on empirical analysis. The final parameter—the number of topics derived from each batch—is difficult to estimate a priori , and must be made with some care. If k is too small, keywords and tweets belonging to a topic may be difficult to conceptualize as a singular, coherent, and meaningful topic. If k is too large, keywords and tweets belonging to a topic may be too specific and obscure. To determine a reasonable value of k , we ran the NMijF process on each batch with three different levels of the parameter—5, 10, and 20 topics per batch. This process generated three different representations of the corpus: 205, 410, and 820 topics. For each of these representations, each tweet was classified into one (and only one) topic. We represented each topic as a list of ten keywords most prevalent within the tweets of that topic.

Assessing the quality of topical representations

To select a topical representation for further analysis, we inspected the quality of each. Initially, we considered the use of a completely automatic process to assess or produce high quality topic derivations. However, our attempts to use completely automated techniques on tweets with a known topic structure failed to produce correct or reasonable solutions. Thus, we assessed quality using human assessment (see Table 1 ). The first stage involved inspecting each topical representation of the corpus (205, 410, and 820 topics), and manually flagging any topics that were clearly problematic. Specifically, we examined each topical representation to determine whether topics represented as separate were in fact distinguishable from one another. We discovered that the 820 topic representation (20 topics per batch) contained many closely related topics.

To quantify the distinctiveness between topics, we compared each topic to each other topic in the same batch in an automated process. If two topics shared three or more (of ten) keywords, these topics were deemed similar. We adopted this threshold from existing topic modeling work (Fang et al., 2016a , b ), and verified it through an informal inspection. We found that pairs of topics below this threshold were less similar than those equal to or above it. Using this threshold, the 820 topic representation was identified as less distinctive than other representations. Of the 41 batches, nine contained at least two similar topics for the 820 topic representation (cf., 0 batches for the 205 topic representation, two batches for the 410 topic representation). As a result, we chose to exclude the representation from further analysis.

The second stage of quality assessment involved inspecting the quality of individual topics. To achieve this, we adopted the pairwise topic preference task outlined by Fang et al. ( 2016a , b ). In this task, raters were shown pairs of two similar topics (represented as ten keywords), one from the 205 topic representation and the other from the 410 topic representation. To assist in their interpretation of topics, raters could also view three tweets belonging to each topic. For each pair of topics, raters indicated which topic they believed was superior, on the basis of coherency, meaning, interpretability, and the related tweets (see Table 1 ). Through aggregating responses, a relative measure of quality could be derived.

Initially, members of the research team assessed 24 pairs of topics. Results from the task did not indicate a marked preference for either topical representation. To confirm this impression more objectively, we recruited participants from the Australian community as raters. We used Qualtrics—an online survey platform and recruitment service—to recruit 154 Australian participants, matched with the general Australian population on age and gender. Each participant completed judgments on 12 pairs of similar topics (see Supplementary Material for further information).

Participants generally preferred the 410 topic representation over the 205 topic representation ( M = 6.45 of 12 judgments, S D = 1.87). Of 154 participants, 35 were classified as indifferent (selected both topic representations an equal number of times), 74 preferred the 410 topic representation (i.e., selected the 410 topic representation more often than the 205 topic representation), and 45 preferred the 205 topic representation (i.e., selected the 205 topic representation more often that the 410 topic representation). We conducted binomial tests to determine whether the proportion of participants of the three just described types differed reliably from chance levels (0.33). The proportion of indifferent participants (0.23) was reliably lower than chance ( p = 0.005), whereas the proportion of participants preferring the 205 topic solution (0.29) did not differ reliably from chance levels ( p = 0.305). Critically, the proportion of participants preferring the 410 topic solution (0.48) was reliably higher than expected by chance ( p < 0.001). Overall, this pattern indicates a participant preference for the 410 topic representation over the 205 topic representation.

In summary, no topical representation was unequivocally superior. On a batch level, the 410 topic representation contained more batches of non-distinct topic solutions than the 205 topic representation, indicating that the 205 topic representation contained topics which were more distinct. In contrast, on the level of individual topics, the 410 topic representation was preferred by human raters. We use this information, in conjunction with the utility of corresponding aligned topics (see below), to decide which representation is most suitable for our research purposes.

Grouping similar topics repeated in different batches

We were most interested in topics which occurred throughout the year (i.e., in multiple batches) to identify the most stable components of climate change commentary (phase 3). We grouped similar topics from different batches using a topical alignment algorithm (see Chuang et al. 2015 ). This process requires a similarity metric and a similarity threshold. The similarity metric represents the similarity between two topics, which we specified as the proportion of shared keywords (from 0, no keywords shared, to 1, all ten keywords shared). The similarity threshold is a value below which two topics were deemed dissimilar. As above, we set the threshold to 0.3 (three of ten keywords shared)—if two topics shared two or fewer keywords, the topics could not be justifiably classified as similar. To delineate important topics, groups of topics, and other concepts we have provided a glossary of terms in Table 2 .

The topic alignment algorithm is initialized by assigning each topic to its own group. The alignment algorithm iteratively merges the two most similar groups, where the similarity between groups is the maximum similarity between a topic belonging to one group and another topic belonging to the other. Only topics from different groups (by definition, topics from the same group are already grouped as similar) and different batches (by definition, topics from the same batch cannot be similar) can be grouped. This process continues, merging similar groups until no compatible groups remain. We found our initial implementation generated groups of largely dissimilar topics. To address this, we introduced an additional constraint—groups could only be merged if the mean similarity between pairs of topics (each belonging to the two groups in question) was greater than the similarity threshold. This process produced groups of similar topics. Functionally, this allowed us to detect topics repeated throughout the year.

We ran the topical alignment algorithm across both the 205 and 410 topic representations. For the 205 and 410 topic representation respectively, 22.47 and 31.60% of tweets were not associated with topics that aligned with others. This exemplifies the ephemeral and dynamic attributes of Twitter activity: over time, the content of tweets shifts, with some topics appearing only once throughout the year (i.e., in only one batch). In contrast, we identified 42 groups (69.77% of topics) and 101 groups (62.93% of topics) of related topics for the 205 and 410 topic representations respectively, occurring across different time periods (i.e., in more than one batch). Thus, both representations contained transient topics (isolated to one batch) and recurrent topics (present in more than one batch, belonging to a group of two or more topics).

Identifying topics most relevant for answering our research question

For the subsequent qualitative analyses, we were primarily interested in topics prevalent throughout the corpus. We operationalized prevalent topic groupings as any grouping of topics that spanned three or more batches. On this basis, 22 (57.50% of tweets) and 36 (35.14% of tweets) groupings of topics were identified as prevalent for the 205 and 410 topic representations, respectively (see Table 3 ). As an example, consider the prevalent topic groupings from the 205 topic representation, shown in Table 3 . Ten topics are united by commentary on the Great Barrier Reef (Group 2)—indicating this facet of climate change commentary was prevalent throughout the year. In contrast, some topics rarely occurred, such as a topic concerning a climate change comic (indicated by the keywords “xkcd” and “comic”) occurring once and twice in the 205 and 410 topic representation, respectively. Although such topics are meaningful and interesting, they are transient aspects of climate change commen tary and less relevant to our research question. In sum, topic modeling and grouping algorithms have allowed us to collate massive amounts of information, and identify components of the corpus most relevant to our qualitative inquiry.

Selecting the most favorable topical representation

At this stage, we have two complete and coherent representations of the corpus topics, and indications of which topics are most relevant to our research question. Although some evidence indicated that the 410 topic representation contains topics of higher quality, the 205 topic representation was more parsimonious on both the level of topics and groups of topics. Thus, we selected the 205 topic representation for further analysis.

Phase 3. Extract a subset of data

Extracting a subset of data from the selected topical representation.

Before qualitative analysis, researchers must extract a subset of data manageable in size. For this process, we concerned ourselves with only the content of prevalent topic groupings, seen in Table 3 . From each of the 22 prevalent topic groupings, we randomly sampled ten tweets. We selected ten tweets as a trade-off between comprehensiveness and feasibility. This thus reduced our data space for qualitative analysis from 201,423 tweets to 220.

Phase 4: Perform qualitative analysis

Perform thematic analysis.

In the final phase of our analysis, we performed a qualitative thematic analysis (TA; Braun & Clarke, 2006 ) on the subset of tweets sampled in phase 3. This analysis generated distinct themes, each of which answers our research question: what are the common topics of Australian’s tweets about climate change? As such, the themes generated through TA are topics. However, unlike the topics derived from the preceding computational approaches, these themes are informed by the human coder’s interpretation of content and are oriented towards our specific research question. This allows the incorporation of important diagnostic information, including the broader socio-political context of discussed events or terms, and an understanding (albeit, sometimes ambiguous) of the underlying latent meaning of tweets.

We selected TA as the approach allows for flexibility in assumptions and philosophical approaches to qualitative inquiries. Moreover, the approach is used to emphasize similarities and differences between units of analysis (i.e., between tweets) and is therefore useful for generating topics. However, TA is typically applied to lengthy interview transcripts or responses to open survey questions, rather than small units of analysis produced through Twitter activity. To ease the application of TA to small units of analysis, we modified the typical TA process (shown in Table 4 ) as follows.

Firstly, when performing phases 1 and 2 of TA, we initially read through each prevalent topic grouping’s tweets sequentially. By doing this, we took advantage of the relative homogeneity of content within topics. That is, tweets sharing the same topic will be more similar in content than tweets belonging to separate topics. When reading ambiguous tweets, we could use the tweet’s topic (and other related topics from the same group) to aid comprehension. Through the scaffold of topic representations, we facilitated the process of interpreting the data, generating initial codes, and deriving themes.

Secondly, the prevalent topic groupings were used to create initial codes and search for themes (TA phase 2 and 3). For example, the groups of topics indicate content of climate change action (group 1), the Great Barrier Reef (group 2), climate change deniers (group 3), and extreme weather (group 5). The keywords characterizing these topics were used as initial codes (e.g., “action”, “Great Barrier Reef”, “Paris Agreement”, “denial”). In sum, the algorithmic output provided us with an initial set of codes and an understanding of the topic structure that can indicate important features of the corpus.

A member of the research team performed this augmented TA to generate themes. A second rater outside of the research team applied the generated themes to the data, and inter-rater agreement was assessed. Following this, the two raters reached a consensus on the theme of each tweet.

Through TA, we inductively generated five distinct themes. We assigned each tweet to one (and only one) theme. A degree of ambiguity is involved in designating themes for tweets, and seven tweets were too ambiguous to subsume into our thematic framework. The remaining 213 tweets were assigned to one of five themes shown in Table 5 .

In an initial application of the coding scheme, the two raters agreed upon 161 (73.181%) of 220 tweets. Inter-rater reliability was satisfactory, Cohen’s κ = 0.648, p < 0.05. An assessment of agreement for each theme is presented in Table 5 . The proportion of agreement is the total proportion of observations where the two coders both agreed: (1) a tweet belonged to the theme, or (2) a tweet did not belong to the theme. The proportion of specific agreement is the conditional probability that a randomly selected rater will assign the theme to a tweet, given that the other rater did (see Supplementary Material for more information). Theme 3, theme 5, and the N/A categorization had lower levels of agreement than the remaining themes, possibly as tweets belonging to themes 3 and 5 often make references to content relevant to other themes.

Theme 1. Climate change action

The theme occurring most often was climate change action, whereby tweets were related to coping with, preparing for, or preventing climate change. Tweets comment on the action (and inaction) of politicians, political parties, and international cooperation between government, and to a lesser degree, industry, media, and the public. The theme encapsulated commentary on: prioritizing climate change action (“ Let’s start working together for real solutions on climate change ”); Footnote 4 relevant strategies and policies to provide such action (“ #OurOcean is absorbing the majority of #climatechange heat. We need #marinereserves to help build resilience. ”); and the undertaking (“ Labor will take action on climate change, cut pollution, secure investment & jobs in a growing renewables industry ”) or disregarding (“ act on Paris not just sign ”) of action.

Often, users were critical of current or anticipated action (or inaction) towards climate change, criticizing approaches by politicians and governments as ineffective (“ Malcolm Turnbull will never have a credible climate change policy ”), Footnote 5 and undesirable (“ Govt: how can we solve this vexed problem of climate change? Helpful bystander: u could not allow a gigantic coal mine. Govt: but srsly how? ”). Predominately, users characterized the government as unjustifiably paralyzed (“ If a foreign country did half the damage to our country as #climatechange we would declare war. ”), without a leadership focused on addressing climate change (“ an election that leaves Australia with no leadership on #climatechange - the issue of our time! ”).

Theme 2. Consequences of climate change

Users commented on the consequences and risks attributed to climate change. This theme may be further categorized into commentary of: physical systems, such as changes in climate, weather, sea ice, and ocean currents (“ Australia experiencing more extreme fire weather, hotter days as climate changes ”); biological systems, such as marine life (particularly, the Great Barrier Reef) and biodiversity (“ Reefs of the future could look like this if we continue to ignore #climatechange ”); human systems (“ You and your friends will die of old age & I’m going to die from climate change ”); and other miscellaneous consequences (“ The reality is, no matter who you supported, or who wins, climate change is going to destroy everything you love ”). Users specified a wide range of risks and impacts on human systems, such as health, cultural diversity, and insurance. Generally, the consequences of climate change were perceived as negative.

Theme 3. Conversations on climate change

Some commentary centered around discussions of climate change communication, debates, art, media, and podcasts. Frequently, these pertained to debates between politicians (“ not so gripping from No Principles Malcolm. Not one mention of climate change in his pitch. ”) and television panel discussions (“ Yes let’s all debate whether climate change is happening... #qanda ”). Footnote 6 Users condemned the climate change discussions of federal government (“ Turnbull gov echoes Stalinist Russia? Australia scrubbed from UN climate change report after government intervention ”), those skeptical of climate change (“ Trouble is climate change deniers use weather info to muddy debate. Careful???????????????? ”), and media (“ Will politicians & MSM hacks ever work out that they cannot spin our way out of the #climatechange crisis? ”). The term “climate change” was critiqued, both by users skeptical of the legitimacy of climate change (“ Weren’t we supposed to call it ‘climate change’ now? Are we back to ‘global warming’ again? What happened? Apart from summer? ”) and by users seeking action (“ Maybe governments will actually listen if we stop saying “extreme weather” & “climate change” & just say the atmosphere is being radicalized ”).

Theme 4. Climate change deniers

The fourth theme involved commentary on individuals or groups who were perceived to deny climate change. Generally, these were politicians and associated political parties, such as: Malcolm Roberts (a climate change skeptic, elected as an Australian Senator in 2016), Malcolm Turnbull, and Donald Trump. Commentary focused on the beliefs and legitimacy of those who deny the science of climate change (“ One Nation’s Malcolm Roberts is in denial about the facts of climate change ”) or support the denial of climate change science (“ Meanwhile in Australia... Malcolm Roberts, funded by climate change skeptic global groups loses the plot when nobody believes his findings ”). Some users advocated attempts to change the beliefs of those who deny climate change science (“ We have a president-elect who doesn’t believe in climate change. Millions of people are going to have to say: Mr. Trump, you are dead wrong ”), whereas others advocated disengaging from conversation entirely (“ You know I just don’t see any point engaging with climate change deniers like Roberts. Ignore him ”). In comparison to other themes, commentary revolved around individuals and their beliefs, rather than the phenomenon of climate change itself.

Theme 5. The legitimacy of climate change and climate science

Using our four-phased framework, we aimed to identify and qualitatively inspect the most enduring aspects of climate change commentary from Australian posts on Twitter in 2016. We achieved this by using computational techniques to model 205 topics of the corpus, and identify and group similar topics that repeatedly occurred throughout the year. From the most relevant topic groupings, we extracted a subsample of tweets and identified five themes with a thematic analysis: climate change action, consequences of climate change, conversations on climate change, climate change deniers, and the legitimacy of climate change and climate science. Overall, we demonstrated the process of using a mixed-methodology that blends qualitative analyses with data science methods to explore social media data.

Our workflow draws on the advantages of both quantitative and qualitative techniques. Without quantitative techniques, it would be impossible to derive topics that apply to the entire corpus. The derived topics are a preliminary map for understanding the corpus, serving as a scaffold upon which we could derive meaningful themes contextualized within the wider socio-political context of Australia in 2016. By incorporating quantitatively-derived topics into the qualitative process, we attempted to construct themes that would generalize to a larger, relevant component of the corpus. The robustness of these themes is corroborated by their association with computationally-derived topics, which repeatedly occurred throughout the year (i.e., prevalent topic groupings). Moreover, four of the five themes have been observed in existing data science analyses of Twitter climate change commentary. Within the literature, the themes of climate change action and consequences of climate change are common (Newman, 2016 ; O’Neill et al., 2015 ; Pathak et al., 2017 ; Pearce, 2014 ; Jang and Hart, 2015 ; Veltri & Atanasova, 2017 ). The themes of the legitimacy of climate change and climate science (Jang & Hart, 2015 ; Newman, 2016 ; O’Neill et al., 2015 ; Pearce, 2014 ) and climate change deniers (Pathak et al., 2017 ) have also been observed. The replication of these themes demonstrates the validity of our findings.

One of the five themes—conversations on climate change—has not been explicitly identified in existing data science analyses of tweets on climate change. Although not explicitly identifying the theme, Kirilenko and Stepchenkova ( 2014 ) found hashtags related to public conversations (e.g., “#qanda”, “#Debates”) were used frequently throughout the year 2012. Similar to the literature, few (if any) topics in our 205 topic solution could be construed as solely relating to the theme of “conversation”. However, as we progressed through the different phases of the framework, the theme became increasingly apparent. By the grouping stage, we identified a collection of topics unified by a keyword relating to debate. The subsequent thematic analysis clearly discerned this theme. The derivation of a theme previously undetected by other data science studies lends credence to the conclusions of Guetterman et al., ( 2018 ), who deduced that supplementing a quantitative approach with a qualitative technique can lead to the generation of more themes than a quantitative approach alone.

The uniqueness of a conversational theme can be accounted for by three potentially contributing factors. Firstly, tweets related to conversations on climate change often contained material pertinent to other themes. The overlap between this theme and others may hinder the capabilities of computational techniques to uniquely cluster these tweets, and undermine the ability of humans to reach agreement when coding content for this theme (indicated by the relatively low proportion of specific agreement in our thematic analysis). Secondly, a conversational theme may only be relevant in election years. Unlike other studies spanning long time periods (Jang and Hart, 2015 ; Veltri & Atanasova, 2017 ), Kirilenko and Stepchenkova ( 2014 ) and our study harvested data from US presidential election years (2012 and 2016, respectively). Moreover, an Australian federal election occurred in our year of observation. The occurrence of national elections and associated political debates may generate more discussion and criticisms of conversations on climate change. Alternatively, the emergence of a conversational theme may be attributable to the Australian panel discussion television program Q & A. The program regularly hosts politicians and other public figures to discuss political issues. Viewers are encouraged to participate by publishing tweets using the hashtag “#qanda”, perhaps prompting viewers to generate uniquely tagged content not otherwise observed in other countries. Importantly, in 2016, Q & A featured a debate on climate change between science communicator Professor Brian Cox and Senator Malcolm Roberts, a prominent climate science skeptic.

Although our four-phased framework capitalizes on both quantitative and qualitative techniques, it still has limitations. Namely, the sparse content relationships between data points (in our case, tweets) can jeopardize the quality and reproducibility of algorithmic results (e.g., Chuang et al., 2015 ). Moreover, computational techniques can require large computing resources. To a degree, our application mitigated these limitations. We adopted a topic modeling algorithm which uses additional dimensions of tweets (social and temporal) to address the influence of term-to-term sparsity (Nugroho et al., 2017 ). To circumvent concerns of computing resources, we partitioned the corpus into batches, modeled the topics in each batch, and grouped similar topics together using another computational technique (Chuang et al., 2015 ).

As a demonstration of our four-phased framework, our application is limited to a single example. For data collection, we were able to draw from the procedures of existing studies which had successfully used keywords to identify climate change tweets. Without an existing literature, identifying diagnostic terms can be difficult. Nevertheless, this demonstration of our four-phased framework exemplifies some of the critical decisions analysts must make when utilizing a mixed-method approach to social media data.

Both qualitative and quantitative researchers can benefit from our four-phased framework. For qualitative researchers, we provide a novel vehicle for addressing their research questions. The diversity and volume of content of social media data may be overwhelming for both the researcher and their method. Through computational techniques, the diversity and scale of data can be managed, allowing researchers to obtain a large volume of data and extract from it a relevant sample to conduct qualitative analyses. Additionally, computational techniques can help researchers explore and comprehend the nature of their data. For the quantitative researcher, our four-phased framework provides a strategy for formally documenting the qualitative interpretations. When applying algorithms, analysts must ultimately make qualitative assessments of the quality and meaning of output. In comparison to the mathematical machinery underpinning these techniques, the qualitative interpretations of algorithmic output are not well-documented. As these qualitative judgments are inseparable from data science, researchers should strive to formalize and document their decisions—our framework provides one means of achieving this goal.

Through the application of our four-phased framework, we contribute to an emerging literature on public perceptions of climate change by providing an in-depth examination of the structure of Australian social media discourse. This insight is useful for communicators and policy makers hoping to understand and engage the Australian online public. Our findings indicate that, within Australian commentary on climate change, a wide variety of messages and sentiment are present. A positive aspect of the commentary is that many users want action on climate change. The time is ripe it would seem for communicators to discuss Australia’s policy response to climate change—the public are listening and they want to be involved in the discussion. Consistent with this, we find some users discussing conversations about climate change as a topic. Yet, in some quarters there is still skepticism about the legitimacy of climate change and climate science, and so there remains a pressing need to implement strategies to persuade members of the Australian public of the reality and urgency of the climate change problem. At the same time, our analyses suggest that climate communicators must counter the sometimes held belief, expressed in our second theme on climate change consequences, that it is already too late to solve the climate problem. Members of the public need to be aware of the gravity of the climate change problem, but they also need powerful self efficacy promoting messages that convince them that we still have time to solve the problem, and that their individual actions matter.

On Twitter, users may precede a phrase with a hashtag (#). This allows users to signify and search for tweets related to a specific theme.

The analysis of this study was preregistered on the Open Science Framework: https://osf.io/mb8kh/ . See the Supplementary Material for a discussion of discrepancies. Analysis scripts and interim results from computational techniques can be found at: https://github.com/AndreottaM/TopicAlignment .

83 tweets were rendered empty and discarded from the corpus.

The content of tweet are reported verbatim. Sensitive information is redacted.

Malcolm Turnbull was the Prime Minister of Australia during the year 2016.

“ #qanda ” is a hashtag used to refer to Q & A, an Australian panel discussion television program.

Commonwealth Scientific and Industrial Research Organisation (CSIRO) is the national scientific research agency of Australia.

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PMC10476631

The Impact of Social Media on the Mental Health of Adolescents and Young Adults: A Systematic Review

Abderrahman m khalaf.

1 Psychiatry Department, Saudi Commission for Health Specialties, Ministry of Health, Riyadh, SAU

Abdullah A Alubied

Ahmed m khalaf.

2 College of Medicine, Imam Mohammad Ibn Saud Islamic University, Riyadh, SAU

Abdallah A Rifaey

3 College of Medicine, Almaarefa University, Riyadh, SAU

Adolescents increasingly find it difficult to picture their lives without social media. Practitioners need to be able to assess risk, and social media may be a new component to consider. Although there is limited empirical evidence to support the claim, the perception of the link between social media and mental health is heavily influenced by teenage and professional perspectives. Privacy concerns, cyberbullying, and bad effects on schooling and mental health are all risks associated with this population's usage of social media. However, ethical social media use can expand opportunities for connection and conversation, as well as boost self-esteem, promote health, and gain access to critical medical information. Despite mounting evidence of social media's negative effects on adolescent mental health, there is still a scarcity of empirical research on how teens comprehend social media, particularly as a body of wisdom, or how they might employ wider modern media discourses to express themselves. Youth use cell phones and other forms of media in large numbers, resulting in chronic sleep loss, which has a negative influence on cognitive ability, school performance, and socio-emotional functioning. According to data from several cross-sectional, longitudinal, and empirical research, smartphone and social media use among teenagers relates to an increase in mental distress, self-harming behaviors, and suicidality. Clinicians can work with young people and their families to reduce the hazards of social media and smartphone usage by using open, nonjudgmental, and developmentally appropriate tactics, including education and practical problem-solving.

Introduction and background

Humans are naturally social species that depend on the companionship of others to thrive in life. Thus, while being socially linked with others helps alleviate stress, worry, and melancholy, a lack of social connection can pose major threats to one's mental health [ 1 ]. Over the past 10 years, the rapid emergence of social networking sites like Facebook, Twitter, Instagram, and others has led to some significant changes in how people connect and communicate (Table 1 ). Over one billion people are currently active users of Facebook, the largest social networking website, and it is anticipated that this number will grow significantly over time, especially in developing countries. Facebook is used for both personal and professional interaction, and its deployment has had a number of positive effects on connectivity, idea sharing, and online learning [ 2 ]. Furthermore, the number of social media users globally in 2019 was 3.484 billion, a 9% increase year on year [ 3 ].

Social media applications	Examples
Social networks	Facebook, Twitter, Instagram, Snapchat
Media sharing	WhatsApp, Instagram, YouTube, Snapchat, TikTok
Messengers	Facebook Messenger, WhatsApp, Telegram, Viber, iMessage
Blogging platforms	WordPress, Wikipedia
Discussion forums	Reddit, Twitter
Fitness & lifestyle	Fitbit

Mental health is represented as a state of well-being in which individuals recognize their potential, successfully navigate daily challenges, perform effectively at work, and make a substantial difference in the lives of others [ 4 ]. There is currently debate over the benefits and drawbacks of social media on mental health [ 5 ]. Social networking is an important part of safeguarding our mental health. Mental health, health behavior, physical health, and mortality risk are all affected by the quantity and quality of social contacts [ 5 ].

Social media use and mental health may be related, and the displaced behavior theory could assist in clarifying why. The displaced behavior hypothesis is a psychology theory that suggests people have limited self-control and, when confronted with a challenging or stressful situation, may engage in behaviors that bring instant gratification but are not in accordance with their long-term objectives [ 6 ]. In addition, when people are unable to deal with stress in a healthy way, they may act out in ways that temporarily make them feel better but ultimately harm their long-term goals and wellness [ 7 , 8 ]. In the 1990s, social psychologist Roy Baumeister initially suggested the displaced behavior theory [ 9 ]. Baumeister suggested that self-control is a limited resource that can be drained over time and that when self-control resources are low, people are more likely to engage in impulsive or self-destructive conduct [ 9 ]. This can lead to a cycle of bad behaviors and outcomes, as individuals may engage in behaviors that bring short respite but eventually add to their stress and difficulties [ 9 ]. According to the hypothetical terms, those who participate in sedentary behaviors, including social media, engage in fewer opportunities for in-person social interaction, both of which have been demonstrated to be protective against mental illnesses [ 10 ]. Social theories, on the other hand, discovered that social media use influences mental health by affecting how people interpret, maintain, and interact with their social network [ 4 ].

Numerous studies on social media's effects have been conducted, and it has been proposed that prolonged use of social media sites like Facebook may be linked to negative manifestations and symptoms of depression, anxiety, and stress [ 11 ]. A distinct and important time in a person's life is adolescence. Additionally, risk factors such as family issues, bullying, and social isolation are readily available at this period, and it is crucial to preserve social and emotional growth. The growth of digital technology has affected numerous areas of adolescent lives. Nowadays, teenagers' use of social media is one of their most apparent characteristics. Being socially connected with other people is a typical phenomenon, whether at home, school, or a social gathering, and adolescents are constantly in touch with their classmates via social media accounts. Adolescents are drawn to social networking sites because they allow them to publish pictures, images, and videos on their platforms. It also allows teens to establish friends, discuss ideas, discover new interests, and try out new kinds of self-expression. Users of these platforms can freely like and comment on posts as well as share them without any restrictions. Teenagers now frequently post insulting remarks on social media platforms. Adolescents frequently engage in trolling for amusement without recognizing the potentially harmful consequences. Trolling on these platforms focuses on body shaming, individual abilities, language, and lifestyle, among other things. The effects that result from trolling might cause anxiety, depressive symptoms, stress, feelings of isolation, and suicidal thoughts. The authors explain the influence of social media on teenage well-being through a review of existing literature and provide intervention and preventative measures at the individual, family, and community levels [ 12 ].

Although there is a "generally correlated" link between teen social media use and depression, certain outcomes have been inconsistent (such as the association between time spent on social media and mental health issues), and the data quality is frequently poor [ 13 ]. Browsing social media could increase your risk of self-harm, loneliness, and empathy loss, according to a number of research studies. Other studies either concluded that there is no harm or that some people, such as those who are socially isolated or marginalized, may benefit from using social media [ 10 ]. Because of the rapid expansion of the technological landscape in recent years, social media has become increasingly important in the lives of young people. Social networking has created both enormous new challenges and interesting new opportunities. Research is beginning to indicate how specific social media interactions may impair young people's mental health [ 14 ]. Teenagers could communicate with one another on social media platforms, as well as produce, like, and share content. In most cases, these individuals are categorized as active users. On the other hand, teens can also use social media in a passive manner by "lurking" and focusing entirely on the content that is posted by others. The difference between active and passive social media usage is sometimes criticized as a false dichotomy because it does not necessarily reveal whether a certain activity is goal-oriented or indicative of procrastination [ 15 ]. However, the text provides no justification for why this distinction is wrong [ 16 ]. For instance, one definition of procrastination is engaging in conversation with other people to put off working on a task that is more important. The goal of seeing the information created by other people, as opposed to participating with those same individuals, may be to keep up with the lives of friends. One of the most important distinctions that can be made between the various sorts is whether the usage is social. When it comes to understanding and evaluating all these different applications of digital technology, there are a lot of obstacles to overcome. Combining all digital acts into a single predictor of pleasure would, from both a philosophical and an empirical one, invariably results in a reduction in accuracy [ 17 ].

Methodology

This systematic review was carried out and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement and standard practices in the field. The purpose of this study was to identify studies on the influence of technology, primarily social media, on the psychosocial functioning, health, and well-being of adolescents and young adults.

The MEDLINE bibliographical database, PubMed, Google Scholar, CINAHL (Cumulative Index to Nursing and Allied Health Literature), and Scopus were searched between 1 January 2000 and 30 May 2023. Social media AND mental health AND adolescents AND young adults were included in the search strategy (impact or relation or effect or influence).

Two researchers (AK and AR) separately conducted a literature search utilizing the search method and evaluated the inclusion eligibility of the discovered papers based on their titles and abstracts. Then, the full texts of possibly admissible publications were retrieved and evaluated for inclusion. Disagreements among the researchers were resolved by debate and consensus.

The researchers included studies that examined the impact of technology, primarily social media, on the psychosocial functioning, health, and well-being of adolescents and young adults. We only considered English publications, reviews, longitudinal surveys, and cross-sectional studies. We excluded studies that were not written in English, were not comparative, were case reports, did not report the results of interest, or did not list the authors' names. We also found additional articles by looking at the reference lists of the retrieved articles.

Using a uniform form, the two researchers (AK and AA) extracted the data individually and independently. The extracted data include the author, publication year, study design, sample size and age range, outcome measures, and the most important findings or conclusions.

A narrative synthesis of the findings was used to analyze the data, which required summarizing and presenting the results of the included research in a logical and intelligible manner. Each study's key findings or conclusions were summarized in a table.

Study Selection

A thorough search of electronic databases, including PubMed, Embase, and Cochrane Library, was done from 1 January 2000 to 20 May 2023. Initial research revealed 326 potentially relevant studies. After deleting duplicates and screening titles and abstracts, the eligibility of 34 full-text publications was evaluated. A total of 23 papers were removed for a variety of reasons, including non-comparative studies, case reports, and studies that did not report results of interest (Figure (Figure1 1 ).

An external file that holds a picture, illustration, etc.
Object name is cureus-0015-00000042990-i01.jpg

PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses.

This systematic review identified 11 studies that examined the connection between social media use and depression symptoms in children and adolescents. The research demonstrated a modest but statistically significant association between social media use and depression symptoms. However, this relationship's causality is unclear, and additional study is required to construct explanatory models and hypotheses for inferential studies [ 18 ].

Additional research studied the effects of technology on the psychosocial functioning, health, and well-being of adolescents and young adults. Higher levels of social media usage were connected with worse mental health outcomes [ 19 ], and higher levels of social media use were associated with an increased risk of internalizing and externalizing difficulties among adolescents, especially females [ 20 ]. The use of social media was also connected with body image problems and disordered eating, especially among young women [ 21 ], and social media may be a risk factor for alcohol consumption and associated consequences among adolescents and young adults [ 22 ].

It was discovered that cyberbullying victimization is connected with poorer mental health outcomes in teenagers, including an increased risk of sadness and anxiety [ 23 ]. The use of social media was also connected with more depressive symptoms and excessive reassurance-seeking, but also with greater popularity and perceived social support [ 24 ], as well as appearance comparisons and body image worries, especially among young women [ 25 ]. Children and adolescents' bedtime media device use was substantially related to inadequate sleep quantity, poor sleep quality, and excessive daytime drowsiness [ 26 ].

Online friends can be a significant source of social support, but in-person social support appears to provide greater protection against persecution [ 27 ]. Digital and social media use offers both benefits and risks to the health of children and adolescents, and an individualized family media use plan can help strike a balance between screen time/online time and other activities, set boundaries for accessing content, promote digital literacy, and support open family communication and consistent media use rules (Tables (Tables2, 2 , ,3) 3 ) [ 28 ].

Authors	Year	Study design	Sample size and age range	Outcome measures
McCrae et al. [ ]	2017	Systematic review	11 empirical studies examining the relationship between social media use and depressive symptoms in children and adolescents	Correlation between social media use and depressive symptoms, with limited consensus on phenomena for investigation and causality
Przybylski et al. [ ]	2020	Cross-sectional	National Survey of Children’s Health (NSCH): 50,212 primary caregivers	Psychosocial functioning and digital engagement, including a modified version of the Strengths and Difficulties Questionnaire and caregiver estimates of daily television- and device-based engagement
Riehm et al. [ ]	2019	Longitudinal cohort study	Population Assessment of Tobacco and Health study: 6,595 adolescents aged 12-15 years	Internalizing and externalizing problems assessed via household interviews using audio computer-assisted self-interviewing
Holland and Tiggemann et al. [ ]	2016	Systematic review	20 peer-reviewed articles on social networking sites use and body image and eating disorders	Body image and disordered eating
Moreno et al. [ ]	2016	Review	Studies focused on the intersection of alcohol content and social media	Alcohol behaviors and harms associated with alcohol use
Fisher et al. [ ]	2016	Systematic review and meta-analysis	239 effect sizes from 55 reports, representing responses from 257,678 adolescents	Peer cybervictimization and internalizing and externalizing problems
Nesi and Prinstein [ ]	2015	Longitudinal	619 adolescents aged 14.6 years	Depressive symptoms, frequency of technology use (cell phones, Facebook, and Instagram), excessive reassurance-seeking, technology-based social comparison, and feedback-seeking, and sociometric nominations of popularity
Fardouly and Vartanian [ ]	2016	Review	Correlational and experimental studies on social media usage and body image concerns among young women and men	Body image concerns and appearance comparisons
Carter et al. [ ]	2016	Systematic review and meta-analysis	20 cross-sectional studies involving 125,198 children aged 6-19 years	Bedtime media device use and inadequate sleep quantity, poor sleep quality, and excessive daytime sleepiness
Ybarra et al. [ ]	2015	Cross-sectional	5,542 US adolescents aged 14-19 years	Online and in-person peer victimization and sexual victimization, and the role of social support from online and in-person friends
Chassiakos et al. [ ]	2016	Systematic review	Empirical research on traditional and digital media use and health outcomes in children and adolescents	Opportunities and risks of digital and social media use, including effects on sleep, attention, learning, obesity, depression, exposure to unsafe content and contacts, and privacy

Authors	Main results or conclusions
McCrae et al. [ ]	There is a small but statistically significant correlation between social media use and depressive symptoms in young people, but causality is not clear and further research is needed to develop explanatory models and hypotheses for inferential studies. Qualitative methods can also play an important role in understanding the mental health impact of internet use from young people's perspectives.
Przybylski et al. [ ]	Higher levels of social media use were associated with poorer mental health outcomes, but this relationship was small and may be due to other factors.
Riehm et al. [ ]	Greater social media use was associated with an increased risk of internalizing and externalizing problems among adolescents, particularly among females.
Holland and Tiggemann et al. [ ]	Social media use is associated with body image concerns and disordered eating, particularly among young women.
Moreno et al. [ ]	Social media may be a risk factor for alcohol use and associated harms among adolescents and young adults.
Fisher et al. [ ]	Cyberbullying victimization is associated with poorer mental health outcomes among adolescents, including increased risk of depression and anxiety.
Nesi and Prinstein [ ]	Social media use is associated with greater depressive symptoms and excessive reassurance-seeking, but also with greater popularity and perceived social support.
Fardouly and Vartanian [ ]	Social media use is associated with appearance comparisons and body image concerns, particularly among young women.
Carter et al. [ ]	Bedtime media device use is strongly associated with inadequate sleep quantity, poor sleep quality, and excessive daytime sleepiness in children and adolescents. An integrated approach involving teachers, healthcare providers, and parents is needed to minimize device access and use at bedtime.
Ybarra et al. [ ]	Online friends can be an important source of social support, but in-person social support appears to be more protective against victimization. Online social support did not reduce the odds of any type of victimization assessed.
Chassiakos et al. [ ]	Digital and social media use offers both benefits and risks to the health of children and teenagers. A healthy family media use plan that is individualized for a specific child, teenager, or family can identify an appropriate balance between screen time/online time and other activities, set boundaries for accessing content, guide displays of personal information, encourage age-appropriate critical thinking and digital literacy, and support open family communication and implementation of consistent rules about media use.

Does Social Media Have a Positive or Negative Impact on Adolescents and Young Adults?

Adults frequently blame the media for the problems that younger generations face, conceptually bundling different behaviors and patterns of use under a single term when it comes to using media to increase acceptance or a feeling of community [ 29 , 30 ]. The effects of social media on mental health are complex, as different goals are served by different behaviors and different outcomes are produced by distinct patterns of use [ 31 ]. The numerous ways that people use digital technology are often disregarded by policymakers and the general public, as they are seen as "generic activities" that do not have any specific impact [ 32 ]. Given this, it is crucial to acknowledge the complex nature of the effects that digital technology has on adolescents' mental health [ 19 ]. This empirical uncertainty is made worse by the fact that there are not many documented metrics of how technology is used. Self-reports are the most commonly used method for measuring technology use, but they can be prone to inaccuracy. This is because self-reports are based on people's own perceptions of their behavior, and these perceptions can be inaccurate [ 33 ]. At best, there is simply a weak correlation between self-reported smartphone usage patterns and levels that have been objectively verified [ 34 , 35 ].

When all different kinds of technological use are lumped together into a single behavioral category, not only does the measurement of that category contribute to a loss of precision, but the category also contributes to a loss of precision. To obtain precision, we need to investigate the repercussions of a wide variety of applications, ideally guided by the findings of scientific research [ 36 ]. The findings of this research have frequently been difficult to interpret, with many of them suggesting that using social media may have a somewhat negative but significantly damaging impact on one's mental health [ 36 ]. There is a growing corpus of research that is attempting to provide a more in-depth understanding of the elements that influence the development of mental health, social interaction, and emotional growth in adolescents [ 20 ].

It is challenging to provide a succinct explanation of the effects that social media has on young people because it makes use of a range of different digital approaches [ 37 , 38 ]. To utilize and respond to social media in either an adaptive or maladaptive manner, it is crucial to first have a solid understanding of personal qualities that some children may be more likely to exhibit than others [ 39 ]. In addition to this, the specific behaviors or experiences on social media that put teenagers in danger need to be recognized.

When a previous study particularly questioned teenagers in the United States, the authors found that 31% of them believe the consequences are predominantly good, 45% believe they are neither positive nor harmful, and 24% believe they are unfavorable [ 21 ]. Teens who considered social media beneficial reported that they were able to interact with friends, learn new things, and meet individuals who shared similar interests because of it. Social media is said to enhance the possibility of (i) bullying, (ii) ignoring face-to-face contact, and (iii) obtaining incorrect beliefs about the lives of other people, according to those who believe the ramifications are serious [ 21 ]. In addition, there is the possibility of avoiding depression and suicide by recognizing the warning signs and making use of the information [ 40 ]. A common topic that comes up in this area of research is the connection that should be made between traditional risks and those that can be encountered online. The concept that the digital age and its effects are too sophisticated, rapidly shifting, or nuanced for us to fully comprehend or properly shepherd young people through is being questioned, which challenges the traditional narrative that is sent to parents [ 41 ]. The last thing that needs to be looked at is potential mediators of the link between social factors and teenage depression and suicidality (for example, gender, age, and the participation of parents) [ 22 ].

The Dangers That Come With Young Adults Utilizing Social Media

The experiences that adolescents have with their peers have a substantial impact on the onset and maintenance of psychopathology in those teenagers. Peer relationships in the world of social media can be more frequent, intense, and rapid than in real life [ 42 ]. Previous research [ 22 ] has identified a few distinct types of peer interactions that can take place online as potential risk factors for mental health. Being the target of cyberbullying, also known as cyber victimization, has been shown to relate to greater rates of self-inflicted damage, suicidal ideation, and a variety of other internalizing and externalizing issues [ 43 ]. Additionally, young people may be put in danger by the peer pressure that can be found on social networking platforms [ 44 ]. This can take the form of being rejected by peers, engaging in online fights, or being involved in drama or conflict [ 45 ]. Peer influence processes may also be amplified among teenagers who spend time online, where they have access to a wider diversity of their peers as well as content that could be damaging to them [ 46 ]. If young people are exposed to information on social media that depicts risky behavior, their likelihood of engaging in such behavior themselves (such as drinking or using other drugs) may increase [ 22 ]. It may be simple to gain access to online materials that deal with self-harm and suicide, which may result in an increase in the risk of self-harm among adolescents who are already at risk [ 22 ]. A recent study found that 14.8% of young people who were admitted to mental hospitals because they posed a risk to others or themselves had viewed internet sites that encouraged suicide in the two weeks leading up to their admission [ 24 ]. The research was conducted on young people who were referred to mental hospitals because they constituted a risk to others or themselves [ 24 ]. They prefer to publish pictures of themselves on social networking sites, which results in a steady flow of messages and pictures that are often and painstakingly modified to present people in a favorable light [ 24 ]. This influences certain young individuals, leading them to begin making unfavorable comparisons between themselves and others, whether about their achievements, their abilities, or their appearance [ 47 , 48 ].

There is a correlation between higher levels of social networking in comparison and depressed symptoms in adolescents, according to studies [ 25 ]. When determining how the use of technology impacts the mental health of adolescents, it is essential to consider the issue of displacement. This refers to the question of what other important activities are being replaced by time spent on social media [ 49 ]. It is a well-established fact that the circadian rhythms of children and adolescents have a substantial bearing on both their physical and mental development.

However, past studies have shown a consistent connection between using a mobile device before bed and poorer sleep quality results [ 50 ]. These results include shorter sleep lengths, decreased sleep quality, and daytime tiredness [ 50 ]. Notably, 36% of adolescents claim they wake up at least once over the course of the night to check their electronic devices, and 40% of adolescents say they use a mobile device within five minutes of going to bed [ 25 ]. Because of this, the impact of social media on the quality of sleep continues to be a substantial risk factor for subsequent mental health disorders in young people, making it an essential topic for the continuation of research in this area [ 44 ].

Most studies that have been conducted to investigate the link between using social media and experiencing depression symptoms have concentrated on how frequently and problematically people use social media [ 4 ]. Most of the research that was taken into consideration for this study found a positive and reciprocal link between the use of social media and feelings of depression and, on occasion, suicidal ideation [ 51 , 52 ]. Additionally, it is unknown to what extent the vulnerability of teenagers and the characteristics of substance use affect this connection [ 52 ]. It is also unknown whether other aspects of the environment, such as differences in cultural norms or the advice and support provided by parents, have any bearing on this connection [ 25 ]. Even if it is probable that moderate use relates to improved self-regulation, it is not apparent whether this is the result of intermediate users having naturally greater self-regulation [ 25 ].

Gains From Social Media

Even though most of the debate on young people and new media has centered on potential issues, the unique features of the social media ecosystem have made it feasible to support adolescent mental health in more ways than ever before [ 39 ]. Among other benefits, using social media may present opportunities for humor and entertainment, identity formation, and creative expression [ 53 ]. More mobile devices than ever before are in the hands of teenagers, and they are using social media at never-before-seen levels [ 27 ]. This may not come as a surprise given how strongly young people are drawn to digital devices and the affordances they offer, as well as their heightened craving for novelty, social acceptance, and affinity [ 27 ]. Teenagers are interacting with digital technology for longer periods of time, so it is critical to comprehend the effects of this usage and use new technologies to promote teens' mental health and well-being rather than hurt it [ 53 ]. Considering the ongoing public discussion, we should instead emphasize that digital technology is neither good nor bad in and of itself [ 27 ].

One of the most well-known benefits of social media is social connection; 81% of students say it boosts their sense of connectedness to others. Connecting with friends and family is usually cited by teenagers as the main benefit of social media, and prior research typically supports the notion that doing so improves people's well-being. Social media can be used to increase acceptance or a feeling of community by providing adolescents with opportunities to connect with others who share their interests, beliefs, and experiences [ 29 ]. Digital media has the potential to improve adolescent mental health in a variety of ways, including cutting-edge applications in medical screening, treatment, and prevention [ 28 ]. In terms of screening, past research has suggested that perusing social media pages for signs of melancholy or drug abuse may be viable. More advanced machine-learning approaches have been created to identify mental disease signs on social media, such as depression, post-traumatic stress disorder, and suicidality. Self-report measures are used in most studies currently conducted on adolescent media intake. It is impossible to draw firm conclusions on whether media use precedes and predicts negative effects on mental health because research has only been conducted once. Adults frequently blame the media for the problems that younger generations face [ 30 ]. Because they are cyclical, media panics should not just be attributed to the novel and the unknown. Teenagers' time management, worldview, and social interactions have quickly and dramatically changed as a result of technology. Social media offers a previously unheard-of opportunity to spread awareness of mental health difficulties, and social media-based health promotion programs have been tested for a range of cognitive and behavioral health conditions. Thanks to social media's instant accessibility, extensive possibilities, and ability to reach remote areas, young people with mental health issues have exciting therapy options [ 54 ]. Preliminary data indicate that youth-focused mental health mobile applications are acceptable, but further research is needed to assess their usefulness and effectiveness. Youth now face new opportunities and problems as a result of the growing significance of digital media in their life. An expanding corpus of research suggests that teenagers' use of social media may have an impact on their mental health. But more research is needed [ 18 ] considering how swiftly the digital media landscape is changing.

Conclusions

In the digital era, people efficiently employ technology; it does not "happen" to them. Studies show that the average kid will not be harmed by using digital technology, but that does not mean there are no situations where it could. In this study, we discovered a connection between social media use and adolescent depression. Since cross-sectional research represents the majority, longitudinal studies are required. The social and personal life of young people is heavily influenced by social media. Based on incomplete and contradictory knowledge on young people and digital technology, professional organizations provide guidance to parents, educators, and institutions. If new technologies are necessary to promote social interaction or develop digital and relational (digitally mediated) skills for growing economies, policies restricting teen access to them may be ineffective. The research on the impact of social media on mental health is still in its early stages, and more research is needed before we can make definitive recommendations for parents, educators, or institutions. Reaching young people during times of need and when assistance is required is crucial for their health. The availability of various friendships and services may improve the well-being of teenagers.

The authors have declared that no competing interests exist.

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100+ Quantitative Research Topics For Students

Quantitative research is a research strategy focusing on quantified data collection and analysis processes. This research strategy emphasizes testing theories on various subjects. It also includes collecting and analyzing non-numerical data.

Quantitative research is a common approach in the natural and social sciences , like marketing, business, sociology, chemistry, biology, economics, and psychology. So, if you are fond of statistics and figures, a quantitative research title would be an excellent option for your research proposal or project.

How to Get a Title of Quantitative Research

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Finding a great title is the key to writing a great quantitative research proposal or paper. A title for quantitative research prepares you for success, failure, or mediocre grades. This post features examples of quantitative research titles for all students.

Putting together a research title and quantitative research design is not as easy as some students assume. So, an example topic of quantitative research can help you craft your own. However, even with the examples, you may need some guidelines for personalizing your research project or proposal topics.

So, here are some tips for getting a title for quantitative research:

Consider your area of studies
Look out for relevant subjects in the area
Expert advice may come in handy
Check out some sample quantitative research titles

Making a quantitative research title is easy if you know the qualities of a good title in quantitative research. Reading about how to make a quantitative research title may not help as much as looking at some samples. Looking at a quantitative research example title will give you an idea of where to start.

However, let’s look at some tips for how to make a quantitative research title:

The title should seem interesting to readers
Ensure that the title represents the content of the research paper
Reflect on the tone of the writing in the title
The title should contain important keywords in your chosen subject to help readers find your paper
The title should not be too lengthy
It should be grammatically correct and creative
It must generate curiosity

An excellent quantitative title should be clear, which implies that it should effectively explain the paper and what readers can expect. A research title for quantitative research is the gateway to your article or proposal. So, it should be well thought out. Additionally, it should give you room for extensive topic research.

A sample of quantitative research titles will give you an idea of what a good title for quantitative research looks like. Here are some examples:

What is the correlation between inflation rates and unemployment rates?
Has climate adaptation influenced the mitigation of funds allocation?
Job satisfaction and employee turnover: What is the link?
A look at the relationship between poor households and the development of entrepreneurship skills
Urbanization and economic growth: What is the link between these elements?
Does education achievement influence people’s economic status?
What is the impact of solar electricity on the wholesale energy market?
Debt accumulation and retirement: What is the relationship between these concepts?
Can people with psychiatric disorders develop independent living skills?
Children’s nutrition and its impact on cognitive development

Quantitative research applies to various subjects in the natural and social sciences. Therefore, depending on your intended subject, you have numerous options. Below are some good quantitative research topics for students:

The difference between the colorific intake of men and women in your country
Top strategies used to measure customer satisfaction and how they work
Black Friday sales: are they profitable?
The correlation between estimated target market and practical competitive risk assignment
Are smartphones making us brighter or dumber?
Nuclear families Vs. Joint families: Is there a difference?
What will society look like in the absence of organized religion?
A comparison between carbohydrate weight loss benefits and high carbohydrate diets?
How does emotional stability influence your overall well-being?
The extent of the impact of technology in the communications sector

Creativity is the key to creating a good research topic in quantitative research. Find a good quantitative research topic below:

How much exercise is good for lasting physical well-being?
A comparison of the nutritional therapy uses and contemporary medical approaches
Does sugar intake have a direct impact on diabetes diagnosis?
Education attainment: Does it influence crime rates in society?
Is there an actual link between obesity and cancer rates?
Do kids with siblings have better social skills than those without?
Computer games and their impact on the young generation
Has social media marketing taken over conventional marketing strategies?
The impact of technology development on human relationships and communication
What is the link between drug addiction and age?

Need more quantitative research title examples to inspire you? Here are some quantitative research title examples to look at:

Habitation fragmentation and biodiversity loss: What is the link?
Radiation has affected biodiversity: Assessing its effects
An assessment of the impact of the CORONA virus on global population growth
Is the pandemic truly over, or have human bodies built resistance against the virus?
The ozone hole and its impact on the environment
The greenhouse gas effect: What is it and how has it impacted the atmosphere
GMO crops: are they good or bad for your health?
Is there a direct link between education quality and job attainment?
How have education systems changed from traditional to modern times?
The good and bad impacts of technology on education qualities

Your examiner will give you excellent grades if you come up with a unique title and outstanding content. Here are some quantitative research examples titles.

Online classes: are they helpful or not?
What changes has the global CORONA pandemic had on the population growth curve?
Daily habits influenced by the global pandemic
An analysis of the impact of culture on people’s personalities
How has feminism influenced the education system’s approach to the girl child’s education?
Academic competition: what are its benefits and downsides for students?
Is there a link between education and student integrity?
An analysis of how the education sector can influence a country’s economy
An overview of the link between crime rates and concern for crime
Is there a link between education and obesity?

Research title example quantitative topics when well-thought guarantees a paper that is a good read. Look at the examples below to get started.

What are the impacts of online games on students?
Sex education in schools: how important is it?
Should schools be teaching about safe sex in their sex education classes?
The correlation between extreme parent interference on student academic performance
Is there a real link between academic marks and intelligence?
Teacher feedback: How necessary is it, and how does it help students?
An analysis of modern education systems and their impact on student performance
An overview of the link between academic performance/marks and intelligence
Are grading systems helpful or harmful to students?
What was the impact of the pandemic on students?

Irrespective of the course you take, here are some titles that can fit diverse subjects pretty well. Here are some creative quantitative research title ideas:

A look at the pre-corona and post-corona economy
How are conventional retail businesses fairing against eCommerce sites like Amazon and Shopify?
An evaluation of mortality rates of heart attacks
Effective treatments for cardiovascular issues and their prevention
A comparison of the effectiveness of home care and nursing home care
Strategies for managing effective dissemination of information to modern students
How does educational discrimination influence students’ futures?
The impacts of unfavorable classroom environment and bullying on students and teachers
An overview of the implementation of STEM education to K-12 students
How effective is digital learning?

If your paper addresses a problem, you must present facts that solve the question or tell more about the question. Here are examples of quantitative research titles that will inspire you.

An elaborate study of the influence of telemedicine in healthcare practices
How has scientific innovation influenced the defense or military system?
The link between technology and people’s mental health
Has social media helped create awareness or worsened people’s mental health?
How do engineers promote green technology?
How can engineers raise sustainability in building and structural infrastructures?
An analysis of how decision-making is dependent on someone’s sub-conscious
A comprehensive study of ADHD and its impact on students’ capabilities
The impact of racism on people’s mental health and overall wellbeing
How has the current surge in social activism helped shape people’s relationships?

Are you looking for an example of a quantitative research title? These ten examples below will get you started.

The prevalence of nonverbal communication in social control and people’s interactions
The impacts of stress on people’s behavior in society
A study of the connection between capital structures and corporate strategies
How do changes in credit ratings impact equality returns?
A quantitative analysis of the effect of bond rating changes on stock prices
The impact of semantics on web technology
An analysis of persuasion, propaganda, and marketing impact on individuals
The dominant-firm model: what is it, and how does it apply to your country’s retail sector?
The role of income inequality in economy growth
An examination of juvenile delinquents’ treatment in your country

Excellent Topics For Quantitative Research

Here are some titles for quantitative research you should consider:

Does studying mathematics help implement data safety for businesses
How are art-related subjects interdependent with mathematics?
How do eco-friendly practices in the hospitality industry influence tourism rates?
A deep insight into how people view eco-tourisms
Religion vs. hospitality: Details on their correlation
Has your country’s tourist sector revived after the pandemic?
How effective is non-verbal communication in conveying emotions?
Are there similarities between the English and French vocabulary?
How do politicians use persuasive language in political speeches?
The correlation between popular culture and translation

Here are some quantitative research titles examples for your consideration:

How do world leaders use language to change the emotional climate in their nations?
Extensive research on how linguistics cultivate political buzzwords
The impact of globalization on the global tourism sector
An analysis of the effects of the pandemic on the worldwide hospitality sector
The influence of social media platforms on people’s choice of tourism destinations
Educational tourism: What is it and what you should know about it
Why do college students experience math anxiety?
Is math anxiety a phenomenon?
A guide on effective ways to fight cultural bias in modern society
Creative ways to solve the overpopulation issue

An example of quantitative research topics for 12 th -grade students will come in handy if you want to score a good grade. Here are some of the best ones:

The link between global warming and climate change
What is the greenhouse gas impact on biodiversity and the atmosphere
Has the internet successfully influenced literacy rates in society
The value and downsides of competition for students
A comparison of the education system in first-world and third-world countries
The impact of alcohol addiction on the younger generation
How has social media influenced human relationships?
Has education helped boost feminism among men and women?
Are computers in classrooms beneficial or detrimental to students?
How has social media improved bullying rates among teenagers?

High school students can apply research titles on social issues or other elements, depending on the subject. Let’s look at some quantitative topics for students:

What is the right age to introduce sex education for students
Can extreme punishment help reduce alcohol consumption among teenagers?
Should the government increase the age of sexual consent?
The link between globalization and the local economy collapses
How are global companies influencing local economies?

There are numerous possible quantitative research topics you can write about. Here are some great quantitative research topics examples:

The correlation between video games and crime rates
Do college studies impact future job satisfaction?
What can the education sector do to encourage more college enrollment?
The impact of education on self-esteem
The relationship between income and occupation

You can find inspiration for your research topic from trending affairs on social media or in the news. Such topics will make your research enticing. Find a trending topic for quantitative research example from the list below:

How the country’s economy is fairing after the pandemic
An analysis of the riots by women in Iran and what the women gain to achieve
Is the current US government living up to the voter’s expectations?
How is the war in Ukraine affecting the global economy?
Can social media riots affect political decisions?

A proposal is a paper you write proposing the subject you would like to cover for your research and the research techniques you will apply. If the proposal is approved, it turns to your research topic. Here are some quantitative titles you should consider for your research proposal:

Military support and economic development: What is the impact in developing nations?
How does gun ownership influence crime rates in developed countries?
How can the US government reduce gun violence without influencing people’s rights?
What is the link between school prestige and academic standards?
Is there a scientific link between abortion and the definition of viability?

You can never have too many sample titles. The samples allow you to find a unique title you’re your research or proposal. Find a sample quantitative research title here:

Does weight loss indicate good or poor health?
Should schools do away with grading systems?
The impact of culture on student interactions and personalities
How can parents successfully protect their kids from the dangers of the internet?
Is the US education system better or worse than Europe’s?

If you’re a business major, then you must choose a research title quantitative about business. Let’s look at some research title examples quantitative in business:

Creating shareholder value in business: How important is it?
The changes in credit ratings and their impact on equity returns
The importance of data privacy laws in business operations
How do businesses benefit from e-waste and carbon footprint reduction?
Organizational culture in business: what is its importance?

We Are A Call Away

Interesting, creative, unique, and easy quantitative research topics allow you to explain your paper and make research easy. Therefore, you should not take choosing a research paper or proposal topic lightly. With your topic ready, reach out to us today for excellent research paper writing services .

Quantitative Analysis and the Value of Social Media Investment Research

71 Pages Posted: 14 Aug 2023 Last revised: 18 Jul 2024

University of Kentucky, Gatton College of Business and Economics, Department of Finance & Quantitative Methods

Russell Jame

University of Kentucky - Gatton College of Business and Economics

Date Written: August 14, 2023

We examine the impact of introducing quantitative ratings, which strongly predict returns, on the Seeking Alpha (SA) platform. After the change, SA report recommendations become more aligned with quant ratings, particularly among reports mentioning quant-related terms and reports authored by less quantitatively savvy contributors. Furthermore, both types of reports become significantly stronger predictors of returns. Retail trading also becomes more correlated with quant ratings following the release of SA research reports. The findings suggest that broader access to quantitative analysis enhances the quality of SA contributor's investment recommendations and helps retail investors incorporate quantitative signals into their investment decisions.

Keywords: JEL: G12, G14, G23 Quantitative analysis, investment research, social media, Seeking Alpha

JEL Classification: G12, G14, G23

Suggested Citation: Suggested Citation

University of Kentucky, Gatton College of Business and Economics, Department of Finance & Quantitative Methods ( email )

Lexington, KY 40506 United States

Russell Jame (Contact Author)

University of kentucky - gatton college of business and economics ( email ).

550 South Limestone Lexington, KY 40506 United States

HOME PAGE: http://russelljame.com

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100+ Best Quantitative Research Topics For Students In 2023
Quantitative research is a common approach in the natural and social sciences, like marketing, business, sociology, chemistry, biology, economics, and psychology. So, if you are fond of statistics and figures, a quantitative research title would be an excellent option for your research proposal or project.
Quantitative Analysis and the Value of Social Media Investment Research
The findings suggest that broader access to quantitative analysis enhances the quality of SA contributor's investment recommendations and helps retail investors incorporate quantitative signals into their investment decisions. Keywords: JEL: G12, G14, G23 Quantitative analysis, investment research, social media, Seeking Alpha

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Social impact in social media: A new method to evaluate the social impact of research

Introduction

Literature review

Methods and data presentation

Data collection

Data analysis

Analytical categories and codebook

How to analyse Twitter and Facebook data

Quantitative evidence of social impact in social media