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Qualitative Research Using Social Media

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Do you want to study influencers? Opinions and comments on a set of posts? Look at collections of photos or videos on Instagram? Qualitative Research Using Social Media guides the reader in what different kinds of qualitative research can be applied to social media data. It introduces students, as well as those who are new to the field, to developing and carrying out concrete research projects. The book takes the reader through the stages of choosing data, formulating a research question, and choosing and applying method(s).

Written in a clear and accessible manner with current social media examples throughout, the book provides a step-by-step overview of a range of qualitative methods. These are presented in clear ways to show how to analyze many different types of social media content, including language and visual content such as memes, gifs, photographs, and film clips. Methods examined include critical discourse analysis, content analysis, multimodal analysis, ethnography, and focus groups. Most importantly, the chapters and examples show how to ask the kinds of questions that are relevant for us at this present point in our societies, where social media is highly integrated into how we live. Social media is used for political communication, social activism, as well as commercial activities and mundane everyday things, and it can transform how all these are accomplished and even what they mean.

Drawing on examples from Twitter, Instagram, YouTube, TikTok, Facebook, Snapchat, Reddit, Weibo, and others, this book will be suitable for undergraduate students studying social media research courses in media and communications, as well as other humanities such as linguistics and social science-based degrees.

TABLE OF CONTENTS

Chapter chapter 1 | 24  pages, introduction, chapter chapter 2 | 14  pages, qualitative content analysis, chapter chapter 3 | 19  pages, qualitative visual content analysis, chapter chapter 4 | 21  pages, analyzing social media language with critical discourse analysis, chapter chapter 5 | 24  pages, multimodal critical discourse analysis, chapter chapter 6 | 32  pages, multimodal narrative analysis of video clips, chapter chapter 7 | 15  pages, online ethnography, chapter chapter 8 | 22  pages, focus group interviews, chapter chapter 9 | 4  pages.

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Qualitative Research Using Social Media

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Do you want to study influencers? Opinions and comments on a set of posts? Look at collections of photos or videos on Instagram? Qualitative Research Using Social Media guides the reader in what different kinds of qualitative research can be applied to social media data. It introduces students, as well as those who are new to the field, to developing and carrying out concrete research projects. The book takes the reader through the stages of choosing data, formulating a research question, and choosing and applying method(s). Written in a clear and accessible manner with current social media examples throughout, the book provides a step-by-step overview of a range of qualitative methods. These are presented in clear ways to show how to analyze many different types of social media content, including language and visual content such as memes, gifs, photographs, and film clips. Methods examined include critical discourse analysis, content analysis, multimodal analysis, ethnography, and focus groups. Most importantly, the chapters and examples show how to ask the kinds of questions that are relevant for us at this present point in our societies, where social media is highly integrated into how we live. Social media is used for political communication, social activism, as well as commercial activities and mundane everyday things, and it can transform how all these are accomplished and even what they mean. Drawing on examples from Twitter, Instagram, YouTube, TikTok, Facebook, Snapchat, Reddit, Weibo, and others, this book will be suitable for undergraduate students studying social media research courses in media and communications, as well as other humanities such as linguistics and social science-based degrees.

Table of Contents

Gwen Bouvier is a professor at the Institute of Corpus Linguistics and Applications, Shanghai International Studies University, China. Her main research interest is digital communication, specifically civic debate and activism on social media. Professor Bouvier's publications have drawn on critical discourse analysis, multimodality based on social semiotics, and online ethnography. She is the Associate Editor for the journal Social Semiotics. Joel Rasmussen is a senior lecturer in the School of Humanities, Education and Social Sciences at Örebro University, Sweden. His research focuses on how communication processes shape responsibilities and measures regarding risk and health in organizations and society. He is interested in how public sector institutions are refashioning identity through social media. His work is published in international journals such as Human Relations, Discourse & Communication , Safety Science , PLoS ONE , and others.

Critics' Reviews

''An indispensable guide for those who want to learn about, and practically undertake, qualitative social media research of popular platforms.'' — Professor Per Ledin , Södertörn University and author of Doing Visual Analysis, From Theory to Practice "Bouvier and Rasmussen provide an informative, clearly written and indispensable guide for readers investigating social media data or contemplating doing so. A welcome handbook for all research methods courses that seek to remain informed and up to date." — Paul Cobley , Professor in Language and Media, Middlesex University

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Social Media Methods

• Introduction
• Digital Investigative Ethnography
• 'Open Source' Investigations/ Intelligence (OSINT)

Social Media Research Overview

Research on social media platforms has become common in a variety of disciplines in the social sciences and humanities. This guide is a collection of resources about the variety of methods, tools, and techniques used by the interdisciplinary community conducting research of online spaces. By necessity, this community harnesses methods and techniques that span beyond the normal scope of qualitative research, however, many of the analytical principles align with those of qualitative inquiry.

If you are looking for assistance with this type of research, please use the form below.

"Investigative Digital Ethnography combines elements of investigative journalism with ethnographic observation in a practice useful for academic researchers, policy makers, and the press. It is a particularly useful method for understanding disinformation campaigns. By taking the long-form approach of an investigation, this method may follow breaking news, or be used to analyze a specific case after the immediate event is over. The researcher is ideally tracking one central topic or case and may discover additional components as the investigation progresses. At some point, the gathering of information  must end and the ethnographer must move on to analysis.

The ethnographic method situates people in spaces marked by distinct rituals, beliefs, and cultural production. An ethnographer engages with the subjects to varying degrees, and in the case of digital ethnography, with the traces they leave behind. Observing online communities properly takes time, and the ethnographic process requires a commitment to observation during breaking news events and also during the downtime in between.

This investigative ethnographic method merges the pointed search for specific information that defines journalistic and legal investigation, with the long-term observation that defines ethnography. While an individual investigation may lead to one output in the form of an article, a long-term ethnography composed of many investigations can reveal valuable hidden details that may not have been significant to a single investigation." - Friedberg, “Investigative Digital Ethnography.”

• Investigative Digital Ethnography: Methods for Environmental Modeling A methodological primer from the Harvard Shorenstein Center on Media, Politics and Public Policy.
• Digital Ethnography An overview of the field from the National University of Singapore
• Confronting the Digital An overview of conducting ethnography in a comprehensive way in both physical and digital environments.

Open Sources Intelligence/ Investigations, refers broadly to any type of research or investigation that can legally be collected for readily available public information. For many researchers and practitioners this primarily means online sources such as social media, blogs, etc., however this can include any source of publicly available information. Often, OSINT requires the blending of a variety of disciplinary methods from computer science, journalism, sociology, law etc. and is used widely to document abuses of human rights, monitor disinformation campaigns, study and promote social justice and accountability. Below are resources and tutorials to provide an introduction to OSINT tools and methods.

• Bellingcat's Online Investigation Guide A google sheet with an expansive list of tools sorted by type of research.
• OSINT Bibliography - Bellingcat A bibliography of suggested OSINT resources compiled by Giancarlo Fiorella of Bellingcat.

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.

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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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Andreotta, M., Nugroho, R., Hurlstone, M.J. et al. Analyzing social media data: A mixed-methods framework combining computational and qualitative text analysis. Behav Res 51 , 1766–1781 (2019). https://doi.org/10.3758/s13428-019-01202-8

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• Cambridge Handbook of Qualitative Digital Research
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• Contributors
• Part I Philosophical, Epistemological and Theoretical Considerations
• Part II Methodological Considerations
• Chapter 7 Human Values in a Digital-First World: The Implications for Qualitative Research
• Chapter 8 One Picture to Study One Thousand Words
• Chapter 9 Demystifying the Digital
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• Chapter 11 Social Media Qualitative Research Vignettes
• Chapter 12 Co-Inquiring in a Digital Age
• Part III Illustrative Examples and Emergent Issues

Chapter 11 - Social Media Qualitative Research Vignettes

from Part II - Methodological Considerations

Published online by Cambridge University Press:  08 June 2023

The chapter outlines social media and qualitative research. It describes social media for data collection and different qualitative research approaches to data collection. The chapter describes social media as a phenomenon for research and outlines different levels of social media utilization: individual, work-practice and supra-organizational levels. Vignettes for the different levels are provided and the need for qualitative research concluded.

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• Social Media Qualitative Research Vignettes
• By Alex Wilson , Josh Morton , Boyka Simeonova
• Edited by Boyka Simeonova , University of Leicester , Robert D. Galliers , Bentley University, Massachusetts and Warwick Business School
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Ethical use of social media to facilitate qualitative research

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• 1 Flinders University, Adelaide, South Australia, Australia [email protected].
• 2 University of Melbourne, Melbourne, Victoria, Australia.
• 3 Flinders University, Adelaide, South Australia, Australia.
• PMID: 25212856
• DOI: 10.1177/1049732314549031

Increasingly, qualitative health researchers might consider using social media to facilitate communication with participants. Ambiguity surrounding the potential risks intrinsic to social media could hinder ethical conduct and discourage use of this innovative method. We used some core principles of traditional human research ethics, that is, respect, integrity, and beneficence, to design our photo elicitation research that explored the social influences of drinking alcohol among 34 underage women in metropolitan South Australia. Facebook aided our communication with participants, including correspondence ranging from recruitment to feeding back results and sharing research data. This article outlines the ethical issues we encountered when using Facebook to interact with participants and provides guidance to researchers planning to incorporate social media as a tool in their qualitative studies. In particular, we raise the issues of privacy and confidentiality as contemporary risks associated with research using social media.

Keywords: Internet; alcohol/alcoholism; ethics / moral perspectives; sociology; young adults.

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Social Media in Qualitative Research: Challenges and Recommendations

Information and Organization, vol. 27, issue 2, pages 87-99, 2017, https://doi.org/10.1016/j.infoandorg.2017.03.001.

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Posted: 22 Sep 2021

Brad McKenna

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Date Written: 2017

The emergence of social media on the Internet provides an opportunity for information systems researchers to examine new phenomena in new ways. However, for various reasons qualitative researchers in IS have not fully embraced this opportunity. This paper looks at the potential use of social media in qualitative research in information systems. It discusses some of the challenges of using social media and suggests how qualitative IS researchers can design their studies to capitalize on social media data. After discussing an illustrative qualitative study, the paper makes recommendations for the use of social media in qualitative research in IS. Full paper available at https://doi.org/10.1016/j.infoandorg.2017.03.001.

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Open Access

Peer-reviewed

Research Article

How social media exposure constructs social confidence: An empirical study on impact, mechanisms, and multilateral relationships

Roles Conceptualization, Formal analysis, Methodology, Writing – original draft

Affiliation Department of Journalism, School of Humanities and Arts, Southwestern University of Finance and Economics, Chengdu, China

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

* E-mail: [email protected] , [email protected]

Affiliation Research Institute of Social Development, Southwestern University of Finance and Economics, Chengdu, China

• Yani Liu, 

• Published: September 17, 2024
• https://doi.org/10.1371/journal.pone.0308745
• Reader Comments

Social confidence functions as a vital spiritual force in fostering the positive and healthy evolution of society. This paper explores how social media exposure contributes to the construction of social confidence within the framework of Media-system Dependency Theory. The research unveils the following key findings: (1) Social media exposure positively facilitates social confidence; (2) Group efficacy and group cohesion, perceived as manifestations of cognitive divergence between "efficacy" and "collective" within collective efficacy, both serve as mediating mechanisms influencing the impact of social media exposure on social confidence. The Channel Testing of Causality confirms that group cohesion plays a significantly more crucial role as a pathway compared to group efficacy; (3) The differentiated impact of social media exposure, encompassing Tencent WeChat and Sina Weibo, materializes in distinct components of social confidence and follows different influence pathways.

Citation: Liu Y, Hu K (2024) How social media exposure constructs social confidence: An empirical study on impact, mechanisms, and multilateral relationships. PLoS ONE 19(9): e0308745. https://doi.org/10.1371/journal.pone.0308745

Editor: Xiaoguang Fan, Zhejiang University, CHINA

Received: February 2, 2024; Accepted: July 29, 2024; Published: September 17, 2024

Copyright: © 2024 Liu, Hu. 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 manuscript and its Supporting information files.

Funding: The author(s) received no specific funding for this work.

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

Introduction

A robust social confidence holds significant implications for the health, stability, and sustained development of a society [ 1 ]. In the present era, China’s development is marked by the coexistence of strategic opportunities and concurrent challenges, coupled with a surge in unpredictable and unforeseeable factors. The intricate and ever-changing social environment has contributed to the emergence of societal psychological states such as confusion, anxiety, and depression among the public. To adapt to the constantly evolving new social transition phase and address emerging social risks, social members need to strengthen and boost social confidence. This not only provides the public with a sense of calmness, security, and control, maintaining a positive and upward psychological health status [ 2 ] but also fosters identification with the institutional advantages in the country, enhancing the intention to support, adhere to, and uphold the party and state policies [ 3 ].

Governance of social confidence requires leveraging the propagative and guiding role of new media. People are unable to directly experience various aspects of the real environment and rely on the pseudo-environment constructed by new media to perceive society and form an overall social attitude [ 4 ]. While the media disseminates information about the societal status and changes, maintaining the public’s overall confidence in the social system is its crucial responsibility [ 5 ]. Social media, as the mainstream platform of new media, with its characteristic of "everyone as a medium," not only influences social relationships but also shapes the formation of mimicry environments at various levels [ 6 ]. It holds significant value in constructing social confidence.

Research on social confidence has received widespread attention and active exploration in the academic community. Existing studies primarily unfold in four aspects: firstly, there are descriptions and comparative analyses of the current status of social confidence for specific groups or geographical areas [ 7 ]. Secondly, endeavors have been undertaken to articulate the conceptual underpinnings of social confidence, establishing mature measurement dimensions and indicators [ 8 ]. However, empirical research frequently exhibits a tendency to gauge it through partial dimensions, overlooking its holistic facets. This is apparent in the emphasis on real-life situations during measurement, lacking comprehensive investigations into diverse facets of societal development, and notably neglecting inquiries into the public’s future psychological expectations. Thirdly, there is a concentrated exploration of the potential antecedent variables of social confidence, particularly focusing on individual life experiences [ 9 ], government performance [ 10 ], and other social reality factors. The relationship between the media and social confidence has not received sufficient attention. Fourthly, in the limited exploration by a few scholars into the relationship between mass media and social confidence, the mechanism of the role of perceived media trust has been verified [ 11 ]. However, research validating the effectiveness of other potential mechanism variables is relatively scarce. Upon reviewing the aforementioned related studies, the following shortcomings are identified: an emphasis on real environments rather than mimicry ones, overlooking the constructive role of mass media in shaping social confidence, failure to explore diverse causal pathways between the two, and a lack of systematic measurement in assessing social confidence, thereby limiting the reliability and replicability of existing research findings.

To address the aforementioned research gaps, this paper aims to investigate the impact of social media exposure on social confidence guided by the Media-system Dependency Theory and seeks to validate the explanatory power of collective efficacy in the logic of social confidence formation. Firstly, this paper comprehensively expounds on social confidence from the perspectives of time and social events, utilizing a systematic measurement, thus compensating for past research’s neglect of the temporal characteristics and diverse components of social confidence. This facilitates a correct and comprehensive understanding of the conceptual connotations of social confidence. Secondly, by exploring the impact of heterogeneous social media exposure on social confidence, this paper provides robust evidence to affirm and strengthen the positive predictive role of social media exposure in social confidence. Furthermore, by investigating the mediating role of collective efficacy in constructing social confidence, the paper, while fully recognizing its conceptual divergences including group efficacy and group cohesion, employs mediation testing and Channel Testing of Causality to validate and compare their mechanisms with other potential pathways, thus guiding the identification of critical construction pathways. Additionally, the paper discovers in its exploration of the relationship between heterogeneous social media exposure, including Tencent WeChat and Sina Weibo, and social confidence that the multilateral relationship lies not in the traditional understanding of main effect influences, but in different components of social confidence and influence pathways, thereby extending and deepening the understanding of this multilateral relationship.

Literature review and research hypotheses

Social confidence.

To explore the constructive role of social media in shaping social confidence, it is important to have a correct understanding of the conceptual connotations and characteristics of social confidence. Social confidence refers to a psychological force that enables citizens to believe in the future realization of a certain entity or goal, primarily involving the public’s recognition, trust, psychological state, and the stable psychological expectations formed towards a specific agent, object, or entity [ 12 ]. A systematic examination of existing research reveals that many studies use time or social events as entry points to understand social confidence. From a temporal perspective, social confidence is intricately linked to the societal development status and the process of social change [ 9 ]. The Theory of Perception of Social Change suggests that people’s perceptions of social change include not only an understanding of the current development but also a folk comprehension of developmental patterns and predictions about the future direction of societal development [ 13 ]. Thus, judgment of the current societal conditions and beliefs about future development constitute essential elements of social confidence. However, some studies, while considering the premise of the "future based on reality," overly emphasize the realistic aspects of social confidence, leading to conceptual confusion. For instance, equating social confidence with social trust [ 14 ]. It is noteworthy that temporality is a crucial feature distinguishing social confidence from other social emotions. As a future-oriented emotion, social confidence not only manifests as the influence of present reality on future predictions but also as the feedback effect of the anticipated future on present reality through a sense of certainty [ 15 ]. Therefore, this paper contends that a comprehensive understanding of both the reality and future dimensions of social confidence is unavoidable.

From the perspective of social events, based on the different referents of confidence according to the attitude subject, event-based social confidence primarily includes national-event confidence, societal-event confidence, and personal-event confidence [ 16 ]. These three dimensions, integrated and complementary to each other, are crucial reflections of people’s overall attitudes and opinions toward society. In both qualitative and quantitative research, scholars’ exploration of social confidence under the logic of events is not yet mature. Some researchers use a single dimension or a few key indicators to represent social confidence. Relevant studies mainly revolve around topics such as economic confidence [ 17 ] and party-government confidence [ 18 ]. What’s more, some scholars do not directly focus on social confidence but instead choose proxy variables like life satisfaction for related discussions [ 1 ]. To avoid a one-sided understanding of social confidence, it is necessary to comprehensively understand social confidence in various events and draw on scientifically validated measurement methods from existing mature scales.

Therefore, considering the previous research’s tendency to emphasize solely the present dimensions of social confidence or partial indicators of events, this paper aims to advance the understanding of social confidence in terms of its temporal characteristics and diverse components of events. By incorporating both temporal and social event perspectives, the study comprehensively examines the role of social media in constructing social confidence. Specifically, this paper, considering social confidence under the logic of time as a comprehensive evaluation of various social events during a specific period, we prioritize the understanding of social confidence from a temporal perspective. In robustness tests, we use the event-based perspective for measurement to verify and strengthen the constructive role of social media exposure on social confidence.

Social media exposure and social confidence

Media-system Dependency Theory posits that the foundation of media influence lies in the relationships among the social system, media system, and audience system. The dynamism and complexity of the social environment, along with the resulting uncertainty, lead individuals to rely on the informational resources provided by the media to comprehend society. This dependency results in individuals’ social cognition and attitudes being significantly influenced by the media system [ 19 ]. Social media, defined as a series of network applications built on Web2.0 technology and ideology, enables users to create and communicate user-generated content [ 17 ], thus representing a pivotal aspect of this framework. It highlights two essential elements: social presence and self-presentation [ 20 ], while also offering opportunities for social interaction, learning, and community co-creation [ 21 ]. As a highly popular and engaging form of new media, social media plays a crucial role in shaping social confidence. On the one hand, the convenience and timeliness of social media enable people to access richer news information, allowing the public to form evaluations and expectations of society while understanding its dynamics and public sentiment. On the other hand, considering the embedded network relationships within social media and the networked connections formed under individual goals such as socializing, obtaining information, learning, and collaborating, individuals’ social attitudes are influenced both by the members of the interactive network and by the assimilation effects of the attitudes within their affiliated groups.

Social media’s functional advantage in enhancing social confidence lies in its greater potential to connect heterogeneous subjects and broaden the informational scope of audiences [ 22 ]. Regarding the official opinion field, some scholars argue that the foundational task of constructing social confidence involves clarifying facts and emphasizing the need to address the issue of information asymmetry between the government and the public. By leveraging social media, transparency in the operation of government public affairs can be maintained, ensuring the public’s right to information, enhancing public trust, preventing subjective judgments on societal issues, and suppressing the spread of false information and negative social sentiments [ 12 ]. For the public opinion field, individuals engage in opinion exchange and societal discourse on social media platforms. Through this, they discover and receive support in terms of information, emotions, experiences, and stances. This not only helps individuals resolve confusion and eliminate uncertainty but also raises public awareness about the presence of social groups capable of collectively resisting societal risks. Consequently, this enhances their sense of control over the social environment and confidence in predicting societal developments [ 17 ].

However, some scholars, considering the media environment and its informational characteristics, make competitive judgments contrary to the positive relationship between social media and social confidence mentioned earlier. According to the hypothesis of the "mean world syndrome," the more frequently people are exposed to the mass media, the more they are influenced by its negative information, leading them to perceive the world as violent and dangerous than it actually is [ 23 ]. The media, an important social amplification station of risk, also intensifies people’s awareness of societal risks, resulting in a misjudgment of the overall reality of society. In the context of social media, fragmented social relationships can easily trigger the emergence of disharmonious voices in the network, affecting the stability of public space [ 24 ], exerting inhibitory effects on the factors contributing to the formation of positive social confidence. Also, the distinct "selective disclosure" feature of social media tends to induce "group polarization" in public opinions [ 11 ]. When extreme, irrational, responsibility-weakened, or purposefully polarized opinions continue to ferment or are manipulated by malicious actors, negative social effects arise, subsequently leading to the reverse development of social confidence [ 25 ].

In light of the aforementioned, there is currently a divergence of opinions in academia regarding the relationship between social media exposure and social confidence. Considering the specific context of China, this paper tends to infer a promoting role of social media in shaping social confidence. This inclination arises mainly from the collectivist cultural background in China, where people emphasize relationship-based interdependent selves, and the abundant relational information provided by social media becomes a crucial basis for individuals to define themselves, understand others, and even predict societal trends [ 26 ]. In addition to its economic and public attributes, the media in China carries significant political attributes. Social media, as a highly pervasive and multifaceted information dissemination medium embedded in the societal system, also assumes various governmental functions [ 27 ]. This political nature requires the media to adhere to the correct public opinion guidance, follow positive propaganda policies, and play the roles of "gatekeepers" and "social safety valves." This is not only conducive to alleviating and managing societal emotions, maintaining social harmony and stability but also contributes to propagating the essence of a socially healthy and uplifting nature, actively boosting social confidence [ 28 ]. In conclusion, the following hypothesis is proposed:

• H1: Social media exposure positively influences social confidence.

Building upon the affirmation of the positive predictive role of social media exposure on social confidence, this study proceeds to examine the heterogeneous media effects on social confidence. According to financial report data up to the fourth quarter of 2023, Tencent WeChat boasts a monthly active user count of 1.343 billion, while Sina Weibo has reached 598 million, establishing them as China’s largest and most extensively utilized social media platforms. The primary distinction between WeChat and Weibo lies in their connections within their respective social networks. According to the Strength of Weak Ties, WeChat operates as a strong-tie social network, while Weibo operates as a weak-tie social network [ 29 ]. Specifically, WeChat employs a two-way authentication mechanism, requiring mutual consent between users for connection establishment, primarily consisting of friends, relatives, and coworkers with direct and high interaction in real-life, also known as strong-tie communities [ 30 , 31 ]. In contrast, Weibo utilizes a one-way authentication mechanism, where relationship establishment does not necessitate the permission of the followed user. Interpersonal relationships on Weibo are typically based on common interests or topics, resulting in a more diverse network of connections, also known as weak-tie communities [ 30 , 31 ]. This indicates that compared to WeChat’s personalized and exclusive nature, Weibo’s weak ties offer the public a wider range of information resources and heterogeneous viewpoints [ 32 ].

Some scholars investigating WeChat’s impact on contentious politics have found that discussions often restricted to non-challenging political topics, driven by concerns for reputation protection, information opacity, and interpersonal monitoring [ 33 ]. Similarly, privacy considerations vary across social media platforms. Due to the presence of strong tie audiences on WeChat, individuals tend to idealize themselves and project positive images, limiting their speech tendencies and expressing more neutral and conservative opinions on social events [ 34 ]. Conversely, on Weibo, the public with weak ties mitigates concerns about accountability for their speech, leading to more pronounced and radical public opinions. Therefore, whether in terms of topic restrictions or opinion tendencies, WeChat exposure is more likely to positively influence social confidence compared to Weibo exposure. Despite the diverse information and sometimes controversial public opinions found on Weibo, which may lead to divergent social attitudes and potentially undermine social confidence [ 34 ], exposure to varied viewpoints and perspectives also promotes rational discourse and a comprehensive understanding of societal issues. This, in turn, fosters engagement in public affairs, igniting social trust and a sense of civic responsibility, thus bolstering civic culture and systemic support and ultimately strengthening social confidence [ 35 ].

In addition to individual ordinary netizens mentioned earlier, both WeChat and Weibo also host a large number of self-media and official accounts, which indicate that social media serve not only for interpersonal communication but also for information dissemination and amplification of appeals. Self-media facilitates timely information transmission, collision of diverse thoughts, and accessing social support [ 36 ], but more scholars still argue that it hinders the construction of positive social confidence. Self-media has characteristics such as low entry barriers, uneven staff literacy, simplified information release, and lack of rigorous scrutiny [ 37 ]. In such a free and complex media environment, there is a risk of public opinions being temporarily concealed. As emotionally charged or provocative content continues to ferment, its impactfulness and uncontrollability directly affect public emotions and social order [ 38 , 39 ]. Furthermore, some self-media intentionally fabricate, exaggerate, or distort information for traffic and commercial interests, leading to online rumors that can cause public panic and social unrest in the absence of official information [ 40 ].

However, compared to self-media accounts and other unofficial accounts verified through platform authentication mechanisms, official accounts enjoy higher credibility, which is pivotal for their ability to dominate positive public sentiment [ 11 , 41 ]. Official accounts such as government affairs and news media wield greater authority, enabling them to disseminate verified, accurate, and comprehensive information, thereby correcting misinformation and calming public expectations to promote social stability [ 40 ]. Some scholars also indicate that exposure to official information enhances public political efficacy, political trust, and subjective well-being [ 22 , 42 ]. Importantly, their transparency enhances government credibility, which in turn boosts media credibility, fostering a virtuous cycle that shapes positive social confidence [ 41 ]. In the context of China, official accounts serve as new forms of traditional media, presenting content that aligns with their role as the mouthpieces of the party and government. New media platforms like WeChat and Weibo similarly possess political attributes, serving the interests of power and political systems. Due to their lack of legitimate interviewing rights and regulation by relevant departments, they function solely as tools for aggregating and disseminating official information [ 43 ]. Therefore, despite not being the primary agenda setters, the party and government still manage to dominate public opinion trends through their authority, thus ensuring to some extent the positive development of social mentality [ 44 ].

In summary, after discussing the complex user composition, this paper concludes that although some self-media generate false information and uncontrollable public opinion, WeChat and Weibo generally exhibit a positive trend in public sentiment. This is due to ordinary netizens’ positive self-presentation and perception of efficacy, guidance from official media, and platform regulation, which collectively enhance social confidence. Therefore, the following hypotheses are proposed:

• H2a: WeChat exposure positively influences social confidence.
• H2b: Weibo exposure positively influences social confidence.

Mechanisms: The mediating role of collective efficacy

Research indicates that social confidence is the outcome of mutual influence among individuals, and the complex network relationships between people form the structural basis of social confidence [ 16 ]. The accurate interpretation of the formation logic of social confidence emphasizes understanding the impact pathway of social media exposure on social confidence from the perspective of "individuals within the group." Collective efficacy, a collective sense of power with the potential to shape and transform society, involves group members believing in their ability to alter the situation and destiny of their society, in turn fostering a relatively positive social psychological state.

Tracing the evolutionary process of research on collective efficacy reveals a shift in theoretical exploration within the academic community—from a simple emphasis on the intensity of network relationships to a focus on goal-oriented community mobilization capacity. Two main perspectives have emerged: the first perspective centers on "efficacy," aligning with the mainstream view among scholars based on social cognitive theory. It refers to a group or individual within a group’s perception of their ability to carry out specific actions to achieve expected social outcomes [ 45 , 46 ]. The second perspective emphasizes the "collective" aspect, highlighting the social relationships and structures in which group cognition occurs. It equates collective efficacy with the combination of social cohesion and informal social control orientation, emphasizing mutual attraction among group members and collective commitment to common goals [ 47 ]. In reality, these two perspectives manifest as different emergent states—either focusing on tasks or relationships within the group. It is essential to independently investigate them as distinct factors [ 48 ]. To clarify the referent object as the group in social media rather than the nation or culture, this paper distinguishes collective efficacy into group efficacy and group cohesion, and subsequently explores the mediation roles of both in the relationship between social media exposure and social confidence.

Regarding group efficacy, it plays a significant role in shaping a group’s initiative, effort, and the duration of effort when engaging in specific tasks or contexts [ 49 ]. Social media provides opportunities for the public to engage in continuous, open, and visible discussions on public affairs, fostering group efficacy through positive information exchange and collaborative dialogues [ 50 ]. For citizens with high group efficacy, frequent social interactions offer them more opportunities to understand, coordinate, and share the diverse abilities and resources possessed by group members. By mobilizing various resources to overcome common challenges [ 51 ], individuals with high group efficacy exhibit better receptivity and resilience when facing larger challenges. Consequently, they tend to have relatively optimistic cognitive judgments regarding the completion and performance of group tasks [ 52 ]. Simultaneously, high group efficacy increases people’s involvement and influence in social issues, fostering a supportive attitude toward government public policies and social governance actions aimed at problem-solving [ 53 ]. It also contributes to advancing the realization of national policy goals in the process of triggering informal political participation behaviors among citizens [ 49 ]. Therefore, group efficacy endows individuals with a positive perception of their ability to adapt to and change society, facilitating the establishment of positive social confidence.

Regarding group cohesion, it implies the united trust and embedded group norms among its members. Leveraging the dense social networks and extensive reciprocal exchanges facilitated by social media, individuals come together for common purposes or actions, forming subjective norms and collective commitments through internalization processes [ 47 ]. The democratization of information on social media also provides a potential space for information dissemination and agenda-setting, contributing to the advancement of collective awareness regarding specific social public affairs [ 54 ]. Groups with high cohesion are relatively stable, and individuals affiliated with such groups experience a greater sense of security and less anxiety. Empirical studies have shown that the key to enhancing social confidence in public safety events depends more on individuals’ judgments of social cohesion and moral consensus than on reducing their safety concerns [ 55 ]. Moreover, some scholars argue that cohesive social relationships strengthen people’s identification and perceived legitimacy of the national political system, as well as enhance confidence in the current operation of society [ 56 ]. Therefore, the social endorsement and consensus brought about by group cohesion contribute to maintaining and building public social confidence. In summary, the following hypotheses are proposed:

• H3a: Group efficacy plays a mediating role between social media exposure and social confidence.
• H3b: Group cohesion plays a mediating role between social media exposure and social confidence.

Methodology

Data source.

This study employed a questionnaire survey on the Credamo data platform to collect research data. This platform, affiliated with the Market Research Branch of the China Information Industry Association (CMRA), holds licenses and registration from the Beijing Internet Content Provider (ICP). It complies with industry norms and ethical standards, featuring well-defined user privacy protection policies and stringent data sharing mechanisms. With users across all provinces and regions of China, the platform ensures both reliable data quality and extensive data resources. To ensure respondents’ informed consent, participants were required to click the "Confirm" button after reading the questionnaire introduction, indicating their agreement to participate in the survey. During the pre-survey phase, 50 questionnaires were initially distributed on February 15, 2023, and adjustments were made to questions with ambiguous or inappropriate wording based on feedback from the surveyed individuals and the results of reliability tests. In the formal survey stage, starting from February 17, 2023, until the end of the month, a total of 4 survey rounds were conducted on the Credamo data platform, accumulating 1,267 survey questionnaires (see S1 File ). Two parts of invalid samples were screened and removed: a single-choice screening question (Q21) was set in the questionnaire, "This question is to check if you are answering seriously, please choose ’dissatisfied,‴ and 11 samples that did not meet the response requirements were automatically rejected through system screening. Subsequently, samples displaying signs of non-serious or irrational responses, such as daily media exposure exceeding 24 hours, online exposure time less than social media exposure time, and contradictions in positively and negatively coded questions, were manually screened, resulting in the rejection of an additional 81 samples. After removing invalid questionnaires, a total of 1,175 valid questionnaires were obtained, yielding an effective sample rate of 92.74%. Given the dispersed and complex composition of social media users, absolute random probability sampling presents significant challenges. This study followed the convenient sampling method commonly used by previous scholars on online data platforms, while striving to ensure sample heterogeneity. Sampling results demonstrate the sample’s characteristics of youthfulness and diversified demographic features, such as gender and education, indicating its representativeness overall.

Variable descriptions

Dependent variable: social confidence..

Drawing on Keller et al.’s study [ 3 ], social confidence was assessed through two dimensions: present confidence and future confidence. The former comprised four items: "Our society is capable of addressing future social issues," "The future safety and security of our people are guaranteed," "We live in a secure and reliable era," and the reverse-coded item "Current affairs seem to be increasingly out of control" ( α = 0.775). The latter consisted of two items: "The future society will be functioning normally as well as today" and "Our society has a bright future" ( α = 0.715). Respondents indicated their degree of agreement with these statements on a scale from "completely disagree" to "completely agree," with values assigned from 1 to 6. The measurement of social confidence was derived by averaging the scores of these six items. The average of the first four items represents present confidence, while the average of the last two items indicates future confidence. Subsequent sections utilized mean scores similarly for the measurement of multi-item variables.

Independent variable: Social media exposure.

Following the approach outlined by Lu and Zhu for measuring media usage [ 57 ], respondents indicated the frequency of their "social media usage" with values assigned from 1 to 5 corresponding to "never use," "several times a year," "several times a month," "several times a week," and "several times a day," respectively. To capture heterogeneous social media exposure, WeChat and Weibo exposure were separately quantified. Drawing inspiration from the option design of Question a28 in the 2017 China General Social Survey (CGSS)—China’s earliest nationwide, comprehensive, and continuous academic survey project—responses to the question were coded from 1 to 5, reflecting the spectrum from "never" to "very frequently."

Mediating variables: Group efficacy and group cohesion.

The measurement of group efficacy drew inspiration from studies by Halpern [ 50 ] and Lee [ 58 ]. Questions related to intrinsic group efficacy included "Collective actions have a significant impact on public affairs" and "Collective actions by people can improve society." External group efficacy was assessed through questions such as "If enough people demand change, the government and relevant departments will respond to those demands" and "If enough people demand change, the government and relevant departments will take measures." As for the measurement of group cohesion, modifications were made to the items based on the studies by Browning et al. [ 47 ] and Lee et al. [ 59 ]. These modifications were necessary because previous measurements defined groups as fixed and bounded entities, where individuals within the group shared the same geographical location or objectives. Whereas, online media exposure tends to foster a boundary-less sense of group cohesion. Hence, the adjusted questions included "I am willing to help others," "I have close relationships with people around me," "I get along harmoniously with people around me," "I believe others can be trusted," and "I am willing to stay in touch with people around me in the future" ( α = 0.705). Questions regarding group norms included "There should be clear behavioral norms in the group" and "Members in the group should be clear about their responsibilities, and behave orderly." Response options for the above questions ranged from "completely disagree" to "completely agree" and were coded sequentially from 1 to 5.

Control variables.

Considering the impact of individual characteristics on social confidence, this study controlled for variables such as gender, age, ethnicity, education level, income, residence, political affiliation, and subjective social status. The coding details were as follows: Regarding gender, "female" was coded as 0, and "male" was coded as 1. For age, the original data provided by respondents were used. Ethnicity was coded as "minority" (0) and "Han Chinese" (1). Education level was coded as "below college" (0), "bachelor’s degree" (1), and "master’s degree or above" (2). Monthly income was coded based on ranges: "0–3000 yuan" (1), "3001–5000 yuan" (2), "5001–8000 yuan" (3), "8001–10000 yuan" (4), "10001–15000 yuan" (5), and "above 15001 yuan" (6). Residence was coded as "counties, towns outside the urban area of the county or city, and rural areas" (0), "prefecture-level and county-level urban area" (1), "provincial capital urban area" (2), and "direct-administered municipality urban area" (3). Political affiliation was coded as "Non-Communist Party of China (CPC) member" (0) and "CPC member" (1). Subjective social status was coded from "lowest" to "highest," following the questioning and options in Question a43 (2017 CGSS), with values assigned from 1 to 10. The study addressed sample distribution imbalances (including education level, residence, and income) by merging certain options, and dummy variable treatment was applied during empirical analysis for the former two.

Potential channel variables.

In order to fortify the mediating effects within the causal relationships, Channel Testing of Causality was employed during the robustness check. This "seemingly mediating effect test" involved examining the explanatory power of potential channels on the main effect model, providing tentative evidence for the mediating role of collective efficacy. Considering the explorations into the intrinsic mechanisms of social confidence by scholars such as Liu [ 1 ], Zhang et al. [ 11 ], Bi and Chu [ 18 ], media trust, social capital, and sense of social fairness were selected as potential channel variables. The specific coding methods were as follows: For media trust, based on the quantification method proposed by Su [ 60 ], respondents were asked about their level of trust in official news media, scored using a five-point Likert scale (1 = strongly disagree, 5 = strongly agree). For social capital, following the measurement in Question c46 (2017 CGSS) by Wang and Zhou [ 61 ], respondents were asked about the number of contacts they make through the internet on working days. Options such as "none," "0–4," "5–9," "10–19," "20–49," and "50 or more people" were assigned values from 1 to 6. For the sense of social fairness, based on the measurement question and option design in Question a35 (2017 CGSS) by Su [ 62 ], respondents were asked to evaluate the overall fairness of society (1 = completely unfair, 5 = completely fair). Samples choosing "do not know" (n = 4) were treated by removing them as outliers.

Basic model specification

The choice of the multivariate linear regression model was motivated by two main factors. Firstly, the study’s dependent variable was social confidence, while the independent variables included social media exposure and multiple demographic control variables. Secondly, considering the cross-sectional nature of the data, this model was selected for its ability to effectively manage the complex relationships between multiple independent variables and the dependent variable in a static data setting, demonstrating its suitability for analysis.

Analysis strategy

This study utilized Stata 15 software as the statistical analysis tool. The main analytical methods employed included descriptive statistical analysis and multivariate linear regression analysis. The empirical testing process of the research hypotheses mentioned above comprised three parts: main effect analysis, mediation analysis, and heterogeneity analysis. The first part examined the predictive effect of social media exposure on social confidence. Robustness tests such as variable substitution and propensity score matching were conducted. For the second part, stepwise regression and bootstrap mediation analysis were initially employed to assess the explanatory power of group efficacy and group cohesion as mechanisms for social confidence. Subsequently, Channel Testing of Causality was applied to address potential estimation biases in mediation analysis based on linear regression. The third part examined the direct impact and influence pathways of heterogeneous social media exposure on social confidence.

Ethical considerations

Our observational study utilized a questionnaire survey method, with no manipulation of respondents’ experiences or accounts. Ethical principles outlined in the Declaration of Helsinki were followed: Firstly, respondents were fully informed of the survey’s theme and purpose to ensure informed consent. Anonymous questionnaires were used to maintain confidentiality, and results were strictly used for research purposes without disclosing personal information, safeguarding respondents’ privacy rights. Participation was voluntary, with the option to withdraw at any time. Secondly, questionnaire content was derived from established scales, avoiding subjective composition. Thirdly, all survey questions were reviewed by the Credamo data platform to ensure compliance with ethical standards, with potential violations leading to rejection of distribution requests.

Empirical testing and results analysis

Descriptive statistical analysis.

Table 1 presented the descriptive statistical analysis of the research variables. Preliminary analysis regarding social confidence and social media exposure was shown in Fig 1 . It revealed that respondents’ scores on social confidence exceeded the median of 3.5, and future confidence scores were significantly higher than present confidence scores ( t = 2.451, p <0.05). This indicated that the public was confident in the current social development in China, particularly exhibiting positive expectations for the future. Regarding social media exposure, respondents generally reported frequent usage (M = 4.843, Std = 0.430), especially in the case of WeChat ( t = 36.038, p <0.01). Among all surveyed samples (see Fig 2 ), 40% of respondents were male, with an average age of 32 years. Han Chinese constituted 95% of the respondents, and the majority had attained a bachelor’s degree (72%). Concerning residence, the highest proportion of respondents resided in provincial capital urban areas (41%), followed by prefecture-level and county-level urban areas (35%), while respondents from direct-administered municipalities and areas below the county level were less represented (both accounting for 12%). CPC members made up 31% of the respondents, and income and subjective social status were concentrated at a moderately high level (M = 3.892, M = 5.952).

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https://doi.org/10.1371/journal.pone.0308745.t001

Multivariate linear regression analysis

Table 2 reported the results of the baseline model using multivariate linear regression analysis. The data indicated that social media exposure had a significant positive impact on overall social confidence ( β = 0.222, p <0.01), present confidence ( β = 0.208, p <0.01), and future confidence ( β = 0.251, p <0.01). Therefore, H1 was supported.

https://doi.org/10.1371/journal.pone.0308745.t002

Social confidence was also influenced by individual characteristics: (a) compared to females, males exhibited higher present confidence ( β = 0.109, p <0.01) and overall confidence ( β = 0.082, p <0.05), but no significant difference in future confidence ( β = 0.028, n.s.). This could be attributed to the tendency of females to be more risk-averse, making them more sensitive to the threats and losses posed by current social issues, thereby weakening their present confidence. (b) Social confidence was higher among ethnic minorities than the Han Chinese ( β = -0.172, p <0.05), benefiting from the ideological guidance of the Chinese National Community and the rapid development in minority regions. (c) As age ( β = 0.010, p <0.01), income level ( β = 0.081, p <0.01), and subjective social status ( β = 0.078, p <0.01) increased—consistent with intuitive expectations—public social confidence became more positive. However, future confidence was influenced by subjective social status ( β = 0.094, p <0.01) rather than objective income level ( β = 0.015, n.s.), possibly due to perceived uncertainty about future income or the difficulty in assessing the adaptability of current income levels to future society. (d) Residents in direct-administered municipality urban areas exhibited lower future confidence ( β = -0.173, p <0.1) and overall confidence ( β = -0.142, p <0.1) compared to those in areas below the county level, while residents in other urban areas show no significant differences ( β = 0.056, n.s.; β = 0.049, n.s.). A reasonable inference is that, despite the apparent geographical and socio-economic advantages of direct-administered municipalities, higher population/resource pressure, living costs, and social competition may increase the life pressure of residents, weakening their positive social confidence. As urbanization progresses, areas outside direct-administered municipalities maintain relatively stable living environments, social atmospheres, and resource allocations, resulting in smaller differences in both societal situation and expectations. Therefore, residents in these areas exhibit less disparity in social confidence compared to those in direct-administered municipalities.

Additionally, certain demographic characteristics had no significant impact on social confidence. (a) Concerning education, no significant differences in social confidence were found between "bachelor’s degree" and "master’s degree or above" compared to "below college" ( β = 0.008, n.s.; β = -0.130, n.s.). Although higher education levels implies a higher likelihood of self-confidence, personal success, and positive future expectations, they may have also entailed greater societal expectations and pressures, potentially weakening the influence of education level on social confidence. Another possibility is that some individuals attributed their access to the highest level of education to their own efforts and ambitions while overlooking the significant role of social support systems such as social welfare and government policies. (b) Regarding political affiliation, it did not significantly influence social confidence ( β = -0.010, n.s.). This suggests that the difference between CPC members and non-CPC members lies only in political identity rather than political identification. Especially with the development of online media and the establishment of official accounts by government and relevant administrative departments, the public has more direct and indirect channels for informal political participation. This has, to some extent, increased the understanding and identification of non-CPC members with the ruling party and the state, thereby weakening the confidence difference between them and party members.

Robustness check

Dependent variable replacement..

Social confidence encompasses a dual understanding from both a temporal and an event-based perspective. Considering that the temporal logic of social confidence underlines the characteristics of temporality and comprehensive evaluation, we prefer to embrace this perspective. Subsequently, in the robustness check, we employed event-based logic and the associated measurement method to substitute the dependent variable. Referring to the study by Zhang et al. [ 8 ], overall social confidence (M = 4.086, Std = 0.537, α = 0.918) comprised personal-event confidence (M = 4.089, Std = 0.576, α = 0.863) and societal-event confidence (M = 4.084, Std = 0.567, α = 0.859). The former included 10 items such as personal income, family economic status, housing conditions, health status, job situation, living conditions, family relationships, interpersonal relationships, social status, and development opportunities. The latter encompassed 11 items related to social atmosphere, employment opportunities, social fairness, food safety, public security conditions, social security level, medical service level, education level, price level, infrastructure, and environmental quality. Ratings were conducted using a five-point Likert scale. According to the results presented in Table 3 , social media exposure continues to have a positive influence on social confidence ( β = 0.082, p <0.05; β = 0.091, p <0.01; β = 0.074, p <0.1), indicating its constructive role in fostering social confidence. This finding further supports H1 and is consistent with the main research results.

https://doi.org/10.1371/journal.pone.0308745.t003

Independent variable replacement.

In the previous analysis, this study used the frequency of social media exposure as a measure. However, there might be measurement errors associated with individuals’ subjectively perceived exposure frequency. Therefore, objective measurements were employed as a substitute for the independent variable. Respondents were asked, "On average, how much time do you spend on social media/ internet per day in the past week?" The ratio of the duration of the two was used as an improved measure of social media exposure (M = 0.650, Std = 0.333). The results in Table 4 indicate that the positive correlation between social media exposure and social confidence remains unchanged, whether considering social confidence from a temporal perspective ( β = 0.107, p <0.05; β = 0.099, p <0.1; β = 0.125, p <0.05) or from an event perspective ( β = 0.099, p <0.01; β = 0.078, p <0.05; β = 0.119, p <0.01). This suggests that the findings regarding H1 exhibit good robustness.

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Propensity score matching.

To address endogeneity issues arising from self-selection bias, this study employed Propensity Score Matching (PSM) as a robustness check. The specific principles and steps included: (a) Division into low-level (control group) and high-level social media exposure (treated group) based on its mean. (b) Fitting a binary logit regression model to estimate the probability of high-level social media exposure based on observable characteristics, and obtaining propensity scores from this model. Subsequently, matching was performed on the research sample based on these obtained scores, ensuring comparable observable characteristics between the control and treated groups. (c) Repeating the main study based on the matching results. As shown in Table 5 , the positive impact of social media exposure remained supported, supporting social confidence both from a temporal perspective ( β = 0.217, p <0.01; β = 0.218, p <0.01) and from an event perspective ( β = 0.078, p <0.05; β = 0.080, p <0.05), as well as their respective sub-dimensions. The research findings for H1 remain relatively stable.

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Nonlinear relationship testing.

To explore the potential nonlinear relationship between social media exposure and social confidence, this study included the square term of social media exposure in the baseline model. The results from Table 6 revealed that the square term of social media exposure did not significantly impact social confidence from either a temporal perspective ( β = 0.031, n.s.; β = -0.013, n.s.; β = 0.118, n.s.) or an event-based perspective ( β = -0.043, n.s.; β = -0.060, n.s.; β = -0.026, n.s.). These findings indicated that a nonlinear relationship was not supported. The uniformity in public engagement with social media due to its widespread usage contributed to a consistent direction of influence on social confidence, thus strengthening the robustness of the linear relationship between the two variables.

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Mediation analysis of collective efficacy

To strengthen the causal relationship between social media exposure and social confidence, we conducted an in-depth analysis of the logical formation of social confidence and made inferences about the mediating effect of collective efficacy. The specific methods and procedures are as follows: First, adopting Baron and Kenny’s stepwise approach [ 63 ], the first step examined the total effect ( α 1 ) of social media exposure on social confidence, and the second step tested the significance of the indirect effect ( γ 1 β 2 ), which means the amount of mediation. Second, considering the low testing power caused by the stepwise approach’s sequential tests of γ 1 and β 2 , as well as the often unmet assumption of normal distribution, the bootstrap approach proposed by Preacher and Hayes [ 64 ] was employed. Finally, in reference to the reflections on the application of mediation analysis by Jiang [ 65 ], it was acknowledged that the effect estimates of the above two approaches based on linear regression tests might be biased. This bias could arise from potential confounding factors that simultaneously influence collective efficacy and social confidence. For example, in the case of public trust in the government, individuals who trust the government are likely to obtain public affairs information from both the government and its affiliated media outlets, thereby enhancing their sense of collective efficacy. Simultaneously, they are more inclined to positively acknowledge the government’s performance, contributing to a boost in overall social confidence [ 12 , 66 ]. Additionally, the bias also arise from a potential reciprocal causal relationship between collective efficacy and social confidence, where social confidence, as a social psychological resource, could in turn enhance collective efficacy. Given the potential estimation bias, we further employed a Channel Testing of Causality to provide exploratory evidence for the mediating role of collective efficacy.

Table 7 presented the results of the stepwise mediation analysis. Model 5–2 and 5–3 revealed that social media exposure significantly and positively influenced group efficacy ( β = 0.084, p <0.05) and group cohesion ( β = 0.222, p <0.01). Model 5–4 and 5–5 showed that, after controlling for social media exposure, group efficacy ( β = 0.676, p <0.01) and group cohesion ( β = 0.698, p <0.01) both had a significant and positive impact on social confidence. Additionally, the coefficients associated with social media exposure were smaller than the total effect coefficient observed in Model 5–1. This suggested that both group efficacy and group cohesion can play a mediating role between social media exposure and social confidence. These findings provided preliminary support for H3a and H3b.

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The bootstrap mediation analysis provided direct access to the bootstrap distribution of the indirect effect, allowing for the calculation of standard errors and confidence intervals for the indirect effect. As shown in Table 8 , the indirect effects of group efficacy ( β = 0.057, p <0.05, 95% CI: 0.008–0.105, not including 0) and group cohesion ( β = 0.155, p <0.01, 95% CI: 0.090–0.220, not including 0) as mediating variables were both significant and smaller than the total effect values. This further validated the explanatory power of group efficacy and group cohesion in the process through which social media exposure influenced social confidence. By comparing the magnitudes of the indirect effects, it was evident that group cohesion played a more significant mediating role in this process compared to group efficacy.

https://doi.org/10.1371/journal.pone.0308745.t008

The Channel Testing of Causality helps to better understand the causal relationship between social media exposure and social confidence in terms of explanatory significance and provides support for the identification of the mediating effect of collective efficacy. The principles and steps are as follows: First, examine the effect of social media exposure on social confidence ( α 1 in Eq (2) ), second, re-examine this effect after incorporating the channel variable ( β 1 in Eq (4) ), and finally, compare the numerical changes between the two, revealing the role of channel variables in explaining the impact of social media exposure on social confidence. Table 9 reported the results of the channel approach. Comparing Model 7–1 and 7–2, it was found that the estimated coefficient of social media exposure on social confidence decreased significantly from 0.222 to 0.166, with a reduction of 25.23% (i.e., 1— β 1 / α 1 ). Similarly, according to Model 7–3 to 7–6, after incorporating group cohesion, media trust, social capital, and the sense of social fairness into the model, the reduction percentages of the social media exposure coefficient were 69.82%, 40.54%, 3.60%, and 16.67%, respectively. This indicated that group cohesion, media trust, and collective efficacy can cause larger changes in the coefficient of the impact of social media exposure on social confidence, playing relatively important mediating roles in this process. Particularly, the combination of Model 7–3 and 7–7 showed that the social media exposure coefficient became nonsignificant after controlling for group cohesion, and there was no significant difference compared to the coefficient after controlling for all channel variables, highlighting the crucial role of group cohesion as the most important channel through which social media builds social confidence.

https://doi.org/10.1371/journal.pone.0308745.t009

Social media exposure heterogeneity analysis

In the preceding analysis, the causal relationship and mediating channels between social media exposure and social confidence have been clarified. It is essential to further examine the social confidence effects of heterogeneous social media exposure. According to the results presented in Table 10 , both WeChat exposure ( β = 0.067, p <0.05) and Weibo exposure ( β = 0.085, p <0.01) positively influenced social confidence. The positive effect of WeChat exposure was more pronounced in future confidence ( β = 0.104, p <0.05), while the positive effect of Weibo exposure was significant in both present confidence and future confidence ( β = 0.071, p <0.01; β = 0.113, p <0.01). At this point, H2a was partially supported, and H2b was fully supported.

https://doi.org/10.1371/journal.pone.0308745.t010

This study then employed propensity score matching to examine the heterogeneous media exposure effects, ensuring the robustness of the findings mentioned above. As presented in Table 11 , WeChat exposure significantly influenced overall confidence ( β = 0.065, p <0.05; β = 0.067, p <0.05) and future confidence ( β = 0.104, p <0.05; β = 0.104, p <0.05), but did not significantly affect present confidence ( β = 0.046, n.s.; β = 0.048, n.s.). Meanwhile, Weibo exposure had a significant impact on overall confidence ( β = 0.087, p <0.01; β = 0.082, p <0.01), present confidence ( β = 0.073, p <0.01; β = 0.073, p <0.01), and future confidence ( β = 0.115, p <0.01; β = 0.116, p <0.01), consistent with the aforementioned research findings.

https://doi.org/10.1371/journal.pone.0308745.t011

The above results revealed differences in the components of social confidence arising from heterogeneous media exposure, confirming the multilateral relationship between social media exposure and social confidence, a phenomenon known as the differentiated impact of heterogeneous social media exposure on social confidence [ 5 ]. To gain a deeper understanding of this impact, we further explored the distinct pathways through which heterogeneous social media exposure influences social confidence. The results presented in Table 12 indicated that, regarding the impact process of WeChat exposure on social confidence, only the indirect path through group cohesion was significant (95% CI: 0.025–0.089, not including 0). In contrast, for Weibo exposure, both indirect paths through group efficacy (95% CI: 0.013–0.058) and group cohesion (95% CI: 0.018–0.061) were significant, with neither interval including 0. This verification suggested that there were differentiated pathways in the impact of heterogeneous social media exposure on social confidence.

https://doi.org/10.1371/journal.pone.0308745.t012

Conclusion and discussion

Conclusions.

This study, grounded in the framework of Media-system Dependency Theory, systematically examined the impact of social media exposure on social confidence and its underlying mechanisms. The key findings are as follows:

Firstly, social media exposure positively promotes social confidence. Even after robustness checks such as variable replacement and propensity score matching, this positive effect remains confirmed. The results are consistent with those of Zhang et al. [ 11 ] and Pan and Luo [ 17 ], underscoring the reliability of the positive relationship between social media and social confidence within the Chinese context. However, whether this relationship is applicable in Western countries is questionable. While direct research linking social media to social confidence in Western contexts is scarce, some scholars have noted that internet exposure negatively impacts interpersonal trust and social confidence [ 5 ]. In China, official media representing the party and government timely regulate and guide public opinion trends, suppressing discussions on sensitive political topics while avoiding the emergence of extreme emotions. In contrast, in certain Western countries, values of individualism and liberalism foster more open dialogues, rendering political differences or social conflicts more pronounced or even amplified through social media. This may be the underlying reason for the speculation regarding the negative effects of social media.

Secondly, the influence of social media exposure on social confidence operates through the mechanism of collective efficacy. Group efficacy and group cohesion, focusing on the understanding domains of "efficacy" and "collective," respectively, are regarded as independent and distinct emergent states within the group. Considering them as independent explanatory variables, their explanatory power in the process of social media exposure influencing social confidence has been verified. Notably, the results of the Channel Testing of Causality highlighted that group cohesion is the most crucial channel through which social media exposure affects social confidence. This supports the perspective of David Holmes, who, from a "ritual" standpoint, contends that the primary function of media is not merely to convey information and serve individual interests but to aggregate a broad audience into some form of community, providing them with a sense of belonging.

Thirdly, the differentiated impact of heterogeneous social media exposure on social confidence does not manifest in a consistent direction of influence but rather in the components of social confidence and the influence pathways. Both Wechat and Weibo exposure propel positive developments in social confidence. For the former, this driving force is more evident in the future aspect of social confidence and requires the influence pathway of group cohesion. As for the latter, such media exposure is meaningful for both present and future confidence, with group efficacy and group cohesion serving as effective pathways in this influencing process.

Specifically, WeChat and Weibo both navigate the intricate relationship between social media and social confidence. They can exert both positive and negative influences, yet generally tend to have a positive impact influenced by China’s collectivist culture and political system. On one hand, in WeChat, based on strong ties, the public tends to self-regulate. In contrast, on Weibo, characterized by weak ties, discussions on more diverse topics and less conservative public opinions may undermine social confidence. However, the social understanding and civic responsibility fostered by a relatively free speech environment still contribute positively to social confidence on both platforms. On the other hand, while some self-media outlets may manipulate public opinion and spread rumors for commercial gain, official media, leveraging their credibility, can govern public opinion and promote positive public confidence. This is because in China, media credibility assessment includes not only the professional dimension but also the power dimension, with the latter emphasizing official status, administrative level, and the official nature of content as decisive factors in determining authority [ 37 , 43 ]. Furthermore, both WeChat and Weibo operate within the framework of their political attributes and support for the power system, ensuring that all content aligns with political guidelines [ 43 ]. Therefore, after examining the user composition of WeChat and Weibo, it is concluded that both platforms still play a positive role in influencing social confidence.

According to the Strength of Weak Ties [ 67 ], Weibo’s weak ties can bridge different social groups, facilitating the flow of information, ideas, and beliefs across a wider range. This flow not only provides broader social resources and opportunities for public social mobility, enhancing the perception of possibilities for change in the current situation and the future but also breaks down the limitations and barriers of different social circles, promoting social inclusivity and diversity. Therefore, group efficacy and cohesion are feasible pathways for weak-tie social media to construct social confidence. In contrast, WeChat’s strong ties to some extent confine social circles to offline social networks, leading to concerns about personal identity recognition among the public. This limitation may restrict free speech on social issues and even the willingness to propose suggestions [ 33 ]. Although WeChat may exhibit positive representations of societal attitudes overall, it may actually obscure social conflicts and negative public opinion, hindering the actual resolution of current societal issues. This is because strong-tie social media has no significant impact on group efficacy and present confidence.

Theoretical implications

Firstly, this paper contributes to a deeper understanding of the conceptual essence and scientific measurement of social confidence, while also highlighting the role of social media in promoting it. The study starts by addressing past research’s incomplete understanding of social confidence. Some studies have overemphasized the present dimension of social confidence, treating social trust or present confidence as synonymous with overall social confidence, while neglecting its temporality. Additionally, the use of single or limited indicators to represent social confidence overlooks its multifaceted nature. For instance, equating public attitudes towards government or the economy with overall social confidence fails to consider its diverse event components. Building upon these considerations, this paper comprehensively understands the scientific connotation of social confidence and utilizes mature systematic measurement while fully considering its temporal characteristics and diverse event composition. It affirms the positive constructive role of social media on social confidence from both temporal (including present and future confidence) and event perspectives (including personal-event and societal-event confidence). This not only deepens the understanding of social confidence and provides substantial evidence for the relationship between social media and it but also enriches relevant research on enhancing social confidence in virtual environments rather than real ones.

Secondly, this paper delves into the underlying logic behind the relationship between social media and social confidence, emphasizing the prominent mechanism of group cohesion and providing practical guidance for identifying key pathways in social confidence construction. While existing research extensively acknowledges the mediating role of perceived media trust in this relationship, few studies have examined alternative pathways or conducted comparative analyses. Recognizing social media’s role in facilitating networked relationships among society members, this paper adopts the perspective of "individuals within the group." It first validates the mechanisms of group efficacy and cohesion in constructing social confidence through social media. Subsequently, utilizing the Channel Testing of Causality method, it compares and identifies potential pathways, highlighting the importance of group cohesion, perceived media trust, and group efficacy channels, with particular emphasis on the explanatory power of group cohesion. This research perspective addresses limitations in previous studies, which solely focused on individual cognition or experience as the driving factors of social psychology, underscoring the significance of connections, solidarity, and consensus among individuals in social confidence construction. It offers valuable theoretical and methodological guidance for future exploration in this area.

Thirdly, this paper enhances our grasp of the complex multilateral relationship between heterogeneous social media exposure and social confidence, adding depth to the research framework concerning how social media constructs social confidence. While prior studies comparing the impacts of traditional and electronic media on social confidence have identified the multilateral relationship between media exposure and social confidence, they have predominantly focused on the extent of media influence. Moreover, they have overlooked the multilateral relationship between different types of media within the same category and social confidence. However, this study uncovers that the varying impacts of platforms like WeChat and Weibo on social confidence are not manifested in main effects but rather in diverse components of social confidence and various pathways of influence. These findings significantly contribute to our comprehension of how social media exposure influences social confidence, paving the way for a more holistic understanding of the intricate multilateral relationship between media and social confidence.

Practical implications

Firstly, there is a need for a profound understanding of the multifaceted functional roles that social media plays in constructing social confidence. Individuals are becoming increasingly proactive in expressing public opinions and showcasing societal attitudes on social media platforms, making social confidence more visibly tangible. Simultaneously, this study reveals the significant importance in establishing, aggregating, and boosting social confidence. The government should reinforce the trend towards intelligent governance and enhance the level of intelligent governance. They should leverage emerging information technologies to promptly, accurately, and dynamically grasp online public sentiment and public mentality, establishing a robust monitoring and early warning system for societal psychology. Moreover, governments and relevant regulatory authorities need to effectively utilize social media platforms to disseminate information on public concerns such as healthcare and legal regulations, promptly share policy initiatives, work plans, and achievements with the public to maintain government openness and transparency, and shape a positive government image. These measures will provide active psychological resources and mental support for the sustained and stable operation of the social system.

Furthermore, it is crucial to recognize the significant importance of managing societal emotions and improving psychological expectations to boost public confidence. This stems from the findings of this study, which reveal that social media exposure affects both present and future confidence. Given the inevitable contradictions and conflicts in the process of societal development, it is crucial to prevent the accumulation of hostile emotions during conflicts. Social media, acting as a safety valve for society, should timely release, guide, and intervene in public emotions. This can be achieved by actively guiding reasonable speech and rational empathy through online education or discussion forums, and by regularly collecting, listening to, and responding to public opinions and suggestions through official media accounts. Moreover, efforts to clarify rumors, dispel misunderstandings, establish interactive relationships, and mutual trust between the government and the public are essential for seeking a harmonious coexistence between official public opinion and grassroots public opinion within the same media space. Additionally, social media platforms need to fully acknowledge their social responsibility by enhancing the discernment, regulation, and auditing capabilities of information content, ensuring that the public not only has an accurate understanding of the current development situation in the country but also comprehends the future policy intentions of the party and the government.

Lastly, considering the significant role of social cohesion channels, it is essential to value and leverage the responsibility and influence of social media in fostering consensus and cohesion. Social media subtly influences people’s daily lives and thought patterns; therefore, achieving consensus on societal norms, values, and culture requires joint efforts from the government and the media. The government can utilize social media platforms to strengthen networking between individuals and different groups, facilitating mutual understanding, shared cognition, and consensus on common positions and beliefs. For instance, creating groups for hot public events can stimulate free dialogue and experience sharing among the public. Special attention should be given to preventing the Balkanization of the Internet, thus avoiding consensus dilemmas that may arise from information gaps between groups. Social media platforms should regulate and balance various public issues in the online environment, bridging differences in opinions and perceptions from the stage of information exposure, and achieving a collective commitment for unity and progress among all members of society, thus consolidating the strength for Chinese-style development.

Research limitations and future prospects

This paper has certain limitations and shortcomings, and based on this, future research prospects are proposed. Firstly, when considering the continuity of time and the relative stability of social confidence, people’s present confidence is also based on past social performance and expectations. The cross-sectional data in this paper makes it impossible to discuss the antecedents and processes of social confidence formation in a complete time framework of "past-present-future." Future research could address this limitation by selecting a relatively stable group of respondents and implementing periodic survey and data collection plans to track their levels of social confidence at different time points. This would allow for the observation of the developmental trajectory and dynamic changes in social confidence over time. Moreover, by lagging one period of social confidence, these data could be incorporated into empirical models using dynamic panel data to test the reliability of research results.

Secondly, people’s social cognition is derived from both their personal observations and experiences of society and the mimicry environment constructed by mass media. While this article focuses on social media’s role in shaping social confidence, it does not delve into the impact of individual experiences of societal reality or their combined effects. For example, when individuals encounter unexpected public events, whether the expectations of a better world created by social media will bring comfort and hope or be hindered by personal hardships remains unexplored. Future research could further investigate the joint shaping role of social media and societal practice factors on social confidence. Specifically, incorporating individual experiences and government performance into research models and exploring their respective interactions with social media exposure could be fruitful. We speculate that social media exposure, representing the collective societal mindset, may lead individuals to attribute their predicaments or successes primarily to themselves rather than external societal factors, thus weakening the impact of individual experiences on social confidence. Conversely, social media may magnify the effects of government performance on social confidence, potentially leading to more extreme outcomes.

Thirdly, while this study has incorporated demographic characteristics into the research model and identified the influence of individual features, providing potential explanations for the results, it remains confined to descriptive phenomena and speculative causation. To further deepen theoretical reflections and provide empirical evidence, subsequent research could conduct face-to-face structured interviews with individuals of diverse demographic characteristics. These interviews would aim to uncover deeper insights into their intrinsic social beliefs, values, overall attitudes towards social structure, institutions, policies, social networks and relationships, and expectations for social change and development. Additionally, comparative analyses of social confidence representation across different demographic groups in future studies can offer a more comprehensive understanding of the differences and commonalities among different groups in the formation of social confidence.

Concluding statement

In conclusion, this study significantly enhances our understanding of the role of social media in constructing social confidence within the Chinese context. Despite the concurrent influence of both positive and negative factors, the positive relationship between social media exposure and social confidence stands out, largely influenced by China’s collectivist culture and media political attributes. In this positive relationship, social media plays a pivotal role in fostering public cohesion and consensus-building, departing from previous focal points on factors like media trust and perceived efficacy. When considering heterogeneous social media, there exist more nuanced multilateral relationships between social media and social confidence. Notably, China’s two most prevalent social media platforms, Tencent WeChat and Sina Weibo, impact different components of social confidence and follow distinct pathways of influence. While WeChat, based on strong ties, tends to exhibit positive representations of social confidence, it falls short in imbuing the public with a sense of efficacy in addressing societal issues. Conversely, Weibo, based on weak ties, may feature more extreme opinion trends, yet the opportunities for free speech and spurred civic participation contribute to elevating social confidence.

Supporting information

https://doi.org/10.1371/journal.pone.0308745.s001

S1 File. Questionnaire.

https://doi.org/10.1371/journal.pone.0308745.s002

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Using Social Media to Enable Qualitative Research

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• DOI: 10.1016/J.INFOANDORG.2017.03.001
• Corpus ID: 205433765

Social media in qualitative research: Challenges and recommendations

• Brad McKenna , M. D. Myers , Michael Newman
• Published in Information and organization 1 June 2017
• Computer Science, Sociology

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Why people are becoming addicted to social media: A qualitative study

Maryam chegeni.

1 Social Determinants of Health Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran

2 Department of Biostatistics and Epidemiology, School of Public Health, Kerman University of Medical Sciences, Kerman, Iran

Parvin Mangolian Shahrbabaki

3 Nursing Research Center, Razi Faculty of Nursing and Midwifery, Department of Critical Care Nursing, Kerman University of Medical Sciences, Kerman, Iran

Mahin Eslami Shahrbabaki

4 Neuroscience Research Center, Institute of Neuropharmacology, Shahid Beheshti Hospital, Kerman University of Medical Sciences, Kerman, Iran

Nouzar Nakhaee

5 Neuroscience Research Center, Institute of Neuropharmacology, Kerman University of Medical Sciences, Kerman, Iran

Aliakbar Haghdoost

6 Modeling in Health Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran

BACKGROUND:

Social media addiction (SMA) led to the formation of health-threatening behaviors that can have a negative impact on the quality of life and well-being. Many factors can develop an exaggerated tendency to use social media (SM), which can be prevented in most cases. This study aimed to explore the reasons for SMA.

MATERIALS AND METHODS:

This qualitative study was conducted using content analysis. A total of 18 SM addicted subjects were included through purposive sampling. Data were collected through semi-structured interviews and analyzed using the Lundman and Graneheim qualitative content analysis method. A total of 18 SM addicted subjects were included through purposive sampling. Data were collected through semi-structured interviews and analyzed using the Lundman and Graneheim qualitative content analysis method.

The main category of “weakness in acquiring life skills” was extracted with three themes: “problems in socializing” (including communicating and escaping loneliness), “problems in resiliency” (including devastation in harsh conditions and inability to recover oneself and “lack of problem-solving skills” (including weaknesses in analysis and decision making and disorganization in planning).

CONCLUSIONS:

Weakness in life skills plays an important role in individuals’ addiction to SM and formation of the health-threatening behaviors. Since SMA can affect behavioral health, policymakers must adopt educational and preventive programs to increase the knowledge and skills of individuals in different societies in the modern world.

Introduction

Today, social media (SM) (e.g., WhatsApp, Instagram, Facebook, etc.) have enjoyed such rapidly-growing popularity[ 1 ] that around 2.67 billion users of social networks have been estimated worldwide.[ 2 ] After China, India, and Indonesia, Iran ranks fourth in terms of using SM, having approximately 40 million active online social network users over the past decade, these networks have become part of daily lives,[ 3 ] in a way that people can use them to meet any kind of their daily needs.[ 4 ] Despite their benefits, social networks act as a double-edged sword and can lead to behavioral addiction and irreparable negative effects if their users are unaware and if they are used improperly and purposelessly.[ 5 ] In recent years, excessive and compulsory use of SM has been considered as a behavioral addiction.[ 6 , 7 ] This type of behavioral addiction leads to the formation of health-threatening behaviors and serious harm to physical and mental health.[ 8 ] These health threats include: Dysfunction,[ 9 , 10 ] psychological and well-being disorders,[ 11 , 12 , 13 , 14 ] loss of positive emotions,[ 10 ] loneliness, and decreased social communications,[ 15 ] which may reduce the life quality of users and even their families.

Given the extent and significance of the damages caused by SM addiction (SMA), it is essential to identify experienced reasons and conditions for dependency to prevent possible complications and promote healthy behaviors. On the other hand, trying to change the behavior of others without understanding their underlying causes is doomed to fail.

Thus, investigating the experiences of SM addicts can open a new horizon for policymakers. On the other hand, so far, no study has examined these factors in Iranian culture as well as in the general population of all groups in society. Therefore, based on the views and experiences of people having an addiction to SM, this study aimed to explore factors which increase the likelihood of individuals to indulge in social networks. The results of this study can help develop effective prevention programs.

Materials and Methods

Design and participants.

This study is a qualitative research which builds on conventional content analysis. To gain a deeper understanding of SMA, researchers have immersed themselves in data by gaining direct information from participants.

Using purposive sampling, 18 participants were selected from several prominent psychiatric clinics in Kerman, a city in the South Eastern of Iran. These participants had been diagnosed with an addiction to SM and had experienced its related negative effects. It was attempted to consider the maximum diversity in terms of age, sex, duration of addiction, marital status, education, and family support. The general characteristics of participants in the study are presented in Table 1 .

Demographic characteristics of the study participants

All participants were able to communicate face to face in Farsi. The time and place of the interviews were arranged with participants beforehand, and each interview took about 45–60 min in average.

Semi-structured interviews were performed by the first author in 2019. The participants were requested to answer the questions based on their experiences. Questions included, “What factors made you to tend toward SM?,” “What motivated you into using these social networks?” and “What kind of needs do these networks meet?” During the interviews, it was tried to write down those ambiguities and triggers that came to the researchers’ minds in the form of interview memos to be asked in the subsequent interviews and to clarify the related concepts. All the interviews were recorded. Data collection was continued till a saturation point was reached; that is, a moment when the additional sources of data did not give new information.

Data analysis

The data were analyzed using Graneheim and Lundman's approach. The recorded voices of all interviews were typed verbatim. They were then read several times and coded after extracting semantic units. The semantic units were short and meaningful phrases that were extracted from the participants’ responses. These codes were refined; that is, the similar codes were put together or merged. Thus, a number of subcategories and categories were formed and the hidden content and concepts were extracted. Ultimately, the main meaning of the data was derived, and the themes were arranged to show the hidden content of the data. An example of the developed categories and subcategories based on the refined codes is given in Table 2 .

Example of qualitative content analysis process

The Guba and Lincoln criteria were used to ensure the accuracy and strength of the data.[ 16 ] The researchers’ interpretations of the participants’ responses were shared with them during the interviews to ensure their accuracy as well as to increase data credibility. Further, to evaluate the reliability of the collected data, some parts of the interviews alongside the developed codes were returned to the participants to check the consistency of the ideas extracted by the researchers and the participants. The categories and subcategories extracted from the data were also sent to some experts in the field of qualitative studies to be revised, if required, and agreed upon.

This study was approved by the relevant Ethics Committee (IR.KMU.REC.1397.338). The participants were assured that their information would remain confidential and that, if not further interested, they could leave the interview and refuse to collaborate any longer. After obtaining the written consents, the interviews were conducted individually and at a convenient time and place for the participants.

The 18 participants recruited for the study included, half of whom were women. Their ages ranged between 16 and 41 years. Most of them were single and educated.

The results of the data analysis showed one main theme entitled Weakness in Life Skills, from which three themes were extracted: (1) Problems in socializing including the subthemes of problems in communicating and escaping loneliness; (2) Problems in Resiliency including the subthemes of devastation in harsh conditions and inability to recover oneself (inner distress); and (3) Weakness in Problem-Solving Skills, including the two subthemes of weakness in analysis and decision-making and disorganization in planning [ Table 3 ]. Furthermore, predisposing factors in family and society and attractions of SM extracted of interviews was shown in Figure 1 .

Themes and subthemes extracted from data of participants’ experiences

The categories and subcategories of the causes of social media addict subjects

Problems in socializing

For many participants, weakness in social interactions is a factor that leads them to use SM. Two forms of problems in communicating and escaping loneliness were extracted using this approach.

Problems in communicating

According to the participants’ experiences, one of the reasons for their addictive tendency to SM is their inability to communicate properly. They have trouble even in establishing a simple relationship, avoid face-to-face communication, and often fail to gain experience in social activities. Thus, harmful social behaviors and beliefs replace learning useful social behaviors and beliefs. In order to make up for the lack of real-world effective and useful relationships, they become more inclined to SM and indulge themselves in unreasonable tendencies and hence suffer a great deal of damages. A participant said:

We were a large family and I did not get enough attention. I have very limited social relations. I have an introverted personality and I find SM interesting, because I do not see the other person and I can easily talk (P1) .

Escaping loneliness

Another important reason for most participants was feeling loneliness. Being the only child of a family, being the last child of a family, immigrating, divorcing, and so on were among the factors for their feeling lonely. They were looking for an easy and convenient solution to save themselves from loneliness. Since SM was easily accessible and did not require any specific planning, it was the best available way for them to escape loneliness. A participant said:

I’m living alone and have no siblings. The age difference between my parents and I is too much. So I prefer to go to social networks to fill my time. SM have become part of my life (P10) .

Problems in resiliency

Based on the experiences of the participants, problems in resiliency was another major reason for addiction to tending towards SM and getting addicted to it. The bulk of the problems and the lack of proper support, on the one hand, and the ease of access to SM, on the other hand, have made SM a haven of safety to escape from the crises and to continue their activities there. The use of this reason is examined in two forms: Devastation in harsh conditions and inability to recover oneself.

Devastation in harsh conditions

Participants’ experiences showed that because they lacked self-management skills, they were vulnerable to adverse conditions and get devastated quickly. Most of these participants cannot properly manage their problems and do not succeed in maintaining their bio-psychological balance. Hence, they commit more mistakes in escaping the crises. A participant said:

The love failure that I went through in the SM was unbearable. Just to see if I could forget the previous one, I entered another relationship and hence this vicious cycle was repeated (P7) .

Inability to recover oneself (inner distress)

Most SM addicts have failed in dealing properly with their life problems. They could not recover from those difficulties and could not heal themselves. Such failure has prevented them from successfully going through adverse events and attaining social, educational, and occupational achievements. Most of them have not been able to properly recover from their lives’ adverse events and heal their wounds. Hence, they have succumbed to social harms and may undergo serious hurts such as poor health behaviors. A participant said:

I fell in love with a girl on Instagram. But we broke up after a while. I was seriously hurt. Although I loved football, I didn’t go to work out anymore and I wasn’t selected in talent competitions anymore. I got used to smoking and drinking. Although I’m only 17 years old, I’ve committed suicide twice (P8) .

Lack of problem-solving skills

According to participants’ experiences, lack of problem-solving skills has been one of the key factors in individuals’ addiction to SM. These individuals could not easily solve their problems and consequently suffered from other problems such as depression, lack of concentration and attention, anxiety, and the like. These problems made them more likely to become addicted to SM. The use of this reason is examined in two forms: Weaknesses in analysis and disorganization in planning.

Weaknesses in analysis and decision making

Based on the experiences of the participants, they seemed to lack mature defense mechanisms to defend themselves against life crises. As they were unable to analyze them and find logical solutions, they preferred to choose the easiest way to forget and solve their problems. That is why they went into SM. However, the easiest way is not always the best. As being already vulnerable, they were easily hurt by their wrong decisions. A participant said:

My husband had betrayed me, so I got terribly upset. Instead of finding a wise way, I decided to retaliate. I met a guy in the online SM and got addicted to him. So I was always online. Through these networks, this gentleman came into my life. But he suddenly went away and devastated me. I became inflicted with depression and so I had to see psychiatrist and take medication (P5) .

Disorganization in planning

Based on the experiences of the participants, disorganization in life has been one of their major reasons for addiction to SM. Most of them stated that not only have they been purposeless in their lives, but they have been unable to plan properly and rescue themselves from their problems. Hence, they prefer to go aimlessly into OSM. This has caused them to not only lag behind their daily lives but also to undergo numerous negative effects. A participant said:

I do not have any plan for my future, so I do not see a need for it, why should I work? Why should I study? Having fun is the best plan for me. Many guys are like me; they go online without any purpose, and I spend my time with them (P12) .

The findings showed that one of the main reasons for SMA was a lack of life skills. According to the experiences of the participants, the three most important skills were problems in socialization, problems in resiliency, and lack of problem-solving skills.

The problem in socialization is one of the leading factors in SMA that impede people from receiving enough emotional support and acquiring appropriate social-communicative skills. As a result, their relationships with others decrease and to cope with their sense of loneliness and to get sufficient approval and support from others, they start looking for a place to feel calm. SM, due to their easy access and expansive and variable content, persuade these individuals to go more online. Poor communication skills are one of the most important reasons for spending too much time on social networks. These individuals due to get rid of anxiety and stress of face-to-face interactions, they prefer to use the Internet instead of offline communications to meet their interpersonal needs and relationships.[ 17 , 18 ] In line with the results of the present study, numerous studies have also showed that there is a negative relationship between the level of interpersonal communication skills and Internet addiction and have identified shyness and quality of social communications as strong predictors of Internet addiction, in particular, SMA.[ 18 , 19 , 20 , 21 , 22 , 23 , 24 ] In fact, individuals who have communication problems are less sociable and thus spend many hours on the Internet to communicate with others and prove themselves.[ 25 ]

The participants of the study repeatedly stated that escaping loneliness is a major motivation for their continued presence in online social networks. They are looking for a convenient solution to feel less alone, SM provides them with such opportunity, and they do not even need to take on any commitment and responsibility. To ease their discomforts and compensate for their lack of social interactions, these individuals indulge themselves in social networks and hence lose enough social support in the real world.[ 25 ]

A number of researchers consider resilience as one of the effective factors in preventing addiction to SM.[ 26 , 27 ] This was clearly stated by the participants of the present study. Individuals who are less resilient to problems seem more susceptible to SMA. Because these individuals cannot easily accept and endure griefs and sufferings, they are more likely to be in SM. Hence, they use social networks as a defense mechanism for more comfortable tolerance. However, participants stated that if they had exciting entertainment facilities, exciting entertainments, and a secure and well-paid job, they could easily cope with their problems.

Numerous studies have shown that resilience is an important protective factor against Internet addiction[ 28 , 29 ] SMA[ 26 ] and even drug addiction.[ 30 ] Loneliness is one of the factors leading to addiction. However, resilient people are able to cope with it.[ 31 ] It seems that online activities only reduce the negative emotions of escaping reality. While they do not reinforce social skills to solve relationship problems.[ 21 ]

The study findings showed that most individuals who were dependent on SM could not solve their problems well. For this reason, they suffer from anxiety, depression, and insufficient attention and concentration. To control their problems, they resort to poor solutions such as hanging out in SM, which as stated by themselves, act as a temporary remedy. Some of these individuals do not have any plan for their futures and suffer from disorganization in solving their lives’ problems. Thus, to escape such bitter realities, they become severely addicted to SM. These participants see social networks as a safe haven to forget their problems and sufferings. While they might entangle themselves into other problems. In fact, these networks are not always safe havens. According to a study conducted by Ekinci on Turkish students, individuals who had lower levels of problem-solving skills had higher levels of problematic use of Internet.[ 32 ] Furthermore, in a study conducted by Raiha Aftab, it was found that people who possess problem-solving and coping abilities were less likely to become addicted to Facebook.[ 33 ] Resilient individuals have good problem-solving social skills and adopt positive and rational approaches to problems. Therefore, teaching problem-solving skills can enhance resilience in individuals.[ 34 ]

Evidence obtained from the findings of this study shows the detail of the experiences of Iranian individuals who dependent to SM. Also, our researchers’ efforts was to select diverse groups from the general population. However, due to the limited number of participants in the study and the presence of merely Iranian individuals, it is not possible to examine all the factors affecting SMA. Thus, more expansive quantitative studies are suggested.

Conclusions

Since the present study investigated the factors leading to SMA from the experiences of those involved in these networks, its findings can be quite helpful for prevention and even treatment. It seems that improving the quality of social relationships, purposeful actions, and planning to reduce the sense of loneliness, training, and strengthening problem-solving and resiliency skills in families, schools, and universities can help prevent addiction to SM and subsequently to threatening behaviors physical and mental health.

Financial support and sponsorship

This work was funded by the Kerman University of Medical Sciences under the Research Grant 97000283.

Conflicts of interest

There are no conflicts of interest.

Acknowledgements

The authors extend their appreciation to the participants for their cooperation throughout the study. They also appreciate the assistance of Kerman University of Medical Sciences. This study was part of a Specialty Ph.D. dissertation in epidemiology.