research paper on social behavior

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Collection  29 March 2022

2021 Top 25 Social Sciences and Human Behaviour Articles

We are pleased to share with you the 25 most downloaded  Nature Communications  articles* in social sciences and human behaviour published in 2021. Featuring authors from around the world, these papers highlight valuable research from an international community.

Browse all Top 25 subject area collections  here .

*Data obtained from SN Insights (based on Digital Science's Dimensions) and normalised to account for articles published later in the year.

Research highlights

Psychological characteristics associated with COVID-19 vaccine hesitancy and resistance in Ireland and the United Kingdom

Hesitancy and resistance towards vaccination is a challenge for public health. Here the authors determine psychological characteristics associated with COVID-19 vaccine hesitancy or resistance attitudes in the UK and Ireland.

• Jamie Murphy
• Frédérique Vallières
• Philip Hyland

Policy assessments for the carbon emission flows and sustainability of Bitcoin blockchain operation in China

The growing energy consumption and carbon emissions of Bitcoin mining could potentially undermine global sustainability efforts. Here, the authors show the annual energy consumption of the Bitcoin blockchain in China is expected to peak in 2024 at 296.59 Twh and generate 130.50 million metric tons of carbon emissions.

• Shangrong Jiang
• Shouyang Wang

Potentially long-lasting effects of the pandemic on scientists

The pandemic has caused disruption to many aspects of scientific research. In this Comment the authors describe the findings from surveys of scientists between April 2020 and January 2021, which suggests there was a decline in new projects started in that time.

• Dashun Wang

Unsupervised deep learning identifies semantic disentanglement in single inferotemporal face patch neurons

Little is known about the brain’s computations that enable the recognition of faces. Here, the authors use unsupervised deep learning to show that the brain disentangles faces into semantically meaningful factors, like age or the presence of a smile, at the single neuron level.

• Irina Higgins
• Matthew Botvinick

An actionable anti-racism plan for geoscience organizations

Racism thrives in geoscience. We present an antiracism plan to support the recruitment, retention and success of Black, Indigenous, and other people of color in geoscience. Our action plan can be adapted by any organization to remove barriers to participation for all marginalized geoscientists.

• Hendratta N. Ali
• Sarah L. Sheffield
• Blair Schneider

Neutral bots probe political bias on social media

Social media platforms moderating misinformation have been accused of political bias. Here, the authors use neutral social bots to show that, while there is no strong evidence for such a bias, the content to which Twitter users are exposed depends strongly on the political leaning of early Twitter connections.

• Diogo Pacheco
• Filippo Menczer

Individual differences in information-seeking

Information-seeking is important for learning, social behaviour and decision making. Here the authors investigate factors that associate with individual differences in information-seeking behaviour.

• Christopher. A. Kelly
• Tali Sharot

Lack of consideration of sex and gender in COVID-19 clinical studies

Sex and gender have been associated with differences in SARS-CoV-2 incidence and clinical outcomes and therefore warrant consideration in study designs. Here, the authors assess registered and published clinical COVID-19 studies and find that sex-disaggregated analyses are infrequently presented or planned.

• Mathias Wullum Nielsen
• Sabine Oertelt-Prigione

Optimal COVID-19 quarantine and testing strategies

Safely reducing the necessary duration of quarantine for COVID-19 could lessen the economic impacts of the pandemic. Here, the authors demonstrate that testing on exit from quarantine is more effective than testing on entry, and can enable quarantine to be reduced from fourteen to seven days.

• Chad R. Wells
• Jeffrey P. Townsend
• Alison P. Galvani

Brain network coupling associated with cognitive performance varies as a function of a child’s environment in the ABCD study

Previous research suggests that, for children and adults, there is an association between better performance on cognitive tests and less functional connectivity between two brain networks. Here, the authors find that this association does not hold in a sample of children from households in poverty, highlighting the need for more diverse samples to incorporate a range of childhood environments in developmental cognitive neuroscience.

• Monica E. Ellwood-Lowe
• Susan Whitfield-Gabrieli
• Silvia A. Bunge

mTOR-related synaptic pathology causes autism spectrum disorder-associated functional hyperconnectivity

Autism spectrum disorder (ASD) is characterised by synaptic surplus and atypical functional connectivity. Here, the authors show that synaptic pathology in Tsc2 haploinsufficient mice is associated with autism-like behavior and cortico-striatal hyperconnectivity, and that analogous functional hyperconnectivity signatures can be linked to mTOR-pathway dysfunction in subgroups of children with idiopathic ASD.

• Marco Pagani
• Noemi Barsotti
• Alessandro Gozzi

Cognitive functions and underlying parameters of human brain physiology are associated with chronotype

How being a “morning person” or “evening person” affects human cognition and brain physiology is not well understood. Here the authors show evidence of an association of chronotype with cognitive functions and related physiological parameters.

• Mohammad Ali Salehinejad
• Miles Wischnewski
• Michael A. Nitsche

Deep neural network models reveal interplay of peripheral coding and stimulus statistics in pitch perception

The neural and computational mechanisms underpinning pitch perception remain unclear. Here, the authors trained deep neural networks to estimate the fundamental frequency of sounds and found that human pitch perception depends on precise spike timing in the auditory nerve, but is also adapted to the statistical tendencies of natural sounds.

• Mark R. Saddler
• Ray Gonzalez
• Josh H. McDermott

Sources of confidence in value-based choice

The authors show that metacognitive awareness of choice certainty is closely linked to endogenous attentional states that guide decision behaviour.

• Jeroen Brus
• Helena Aebersold
• Rafael Polania

CDH2 mutation affecting N-cadherin function causes attention-deficit hyperactivity disorder in humans and mice

Molecular mechanisms of attention-deficit hyperactivity disorder (ADHD) are not fully understood. Here the authors demonstrate a mutation in CDH2, encoding N-cadherin, that is associated with ADHD, and in a mouse model, delineate molecular electrophysiological characteristics associated with this mutation.

• D. Halperin

The pupil responds spontaneously to perceived numerosity

Rapid and spontaneous estimation of number is observed in many animals. Here the authors show that perceived number of items modulates the pupillary light response in humans, confirming its spontaneous nature, and introducing pupillometry as a tool to study numerical cognition.

• Elisa Castaldi
• Antonella Pomè
• Paola Binda

Infant gut microbiome composition is associated with non-social fear behavior in a pilot study

Experimental manipulation of the gut microbiome in animal models impacts fear behaviours. Here, the authors show in a pilot study that features of the human infant gut microbiome are associated with non-social fear behaviours during a laboratory based assessment.

• Alexander L. Carlson
• Rebecca C. Knickmeyer

Linear reinforcement learning in planning, grid fields, and cognitive control

Models of decision making have so far been unable to account for how humans’ choices can be flexible yet efficient. Here the authors present a linear reinforcement learning model which explains both flexibility, and rare limitations such as habits, as arising from efficient approximate computation

• Payam Piray
• Nathaniel D. Daw

Predicting lapses of attention with sleep-like slow waves

Attentional lapses occur in many forms such as mind-wandering or mindblanking. Here the authors show different types of attentional lapse are accompanied by slow waves, neural activity that is characteristic of transitions into sleep.

• Thomas Andrillon
• Angus Burns
• Naotsugu Tsuchiya

Shifting parental beliefs about child development to foster parental investments and improve school readiness outcomes

Parents’ investments in their children are a critical input in the production of early skills, yet those investments differ across socioeconomic backgrounds. Here the authors show that variations in parental beliefs about the impact of such investments can be one of the sources of investment disparities, and report interventions that can potentially shift those beliefs.

• John A. List
• Julie Pernaudet
• Dana L. Suskind

Partially overlapping spatial environments trigger reinstatement in hippocampus and schema representations in prefrontal cortex

The authors examine how we differentiate highly similar places from each other. They provide evidence for complementary neural mechanisms in the human hippocampus and prefrontal cortex involved in processing interfering and common elements important to remembering places that we have visited.

• Arne D. Ekstrom

Neural and computational mechanisms of momentary fatigue and persistence in effort-based choice

The willingness to exert effort into demanding tasks often declines over time through fatigue. Here the authors provide a computational account of the moment-to-moment dynamics of fatigue and its impact on effort-based choices, and reveal the neural mechanisms that underlie such computations.

• Tanja Müller
• Miriam C. Klein-Flügge
• Matthew A. J. Apps

Inequality is rising where social network segregation interacts with urban topology

Not much is known about the joint relationships between social network structure, urban geography, and inequality. Here, the authors analyze an online social network and find that the fragmentation of social networks is significantly higher in towns in which residential neighborhoods are divided by physical barriers such as rivers and railroads.

• Johannes Wachs
• Balázs Lengyel

Finding positive meaning in memories of negative events adaptively updates memory

Finding positive meaning in past negative events is associated with enhanced mental health. Here the authors show this adaptively updates memory, leading to enhanced positive emotion and content at future retrieval, which remains two months later.

• Megan E. Speer
• Sandra Ibrahim
• Mauricio R. Delgado

How social relationships shape moral wrongness judgments

Moral judgments depend on relational context, with different normative cooperative expectations – relational norms – embedded in different social relationships, such as parent-child, romantic partners, siblings, or acquaintances. Here, the authors show how relational norms for care, hierarchy, reciprocity, and mating are embedded in a set of everyday social relationships in the United States, and use this information to predict out-of-sample moral judgments in relational context.

• Brian D. Earp
• Killian L. McLoughlin
• Molly J. Crockett

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• Published: 30 January 2021

Understanding students’ behavior in online social networks: a systematic literature review

• Maslin Binti Masrom 1 ,
• Abdelsalam H. Busalim   ORCID: orcid.org/0000-0001-5826-8593 2 ,
• Hassan Abuhassna 3 &
• Nik Hasnaa Nik Mahmood 1  

International Journal of Educational Technology in Higher Education volume  18 , Article number:  6 ( 2021 ) Cite this article

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The use of online social networks (OSNs) has increasingly attracted attention from scholars’ in different disciplines. Recently, student behaviors in online social networks have been extensively examined. However, limited efforts have been made to evaluate and systematically review the current research status to provide insights into previous study findings. Accordingly, this study conducted a systematic literature review on student behavior and OSNs to explicate to what extent students behave on these platforms. This study reviewed 104 studies to discuss the research focus and examine trends along with the important theories and research methods utilized. Moreover, the Stimulus-Organism-Response (SOR) model was utilized to classify the factors that influence student behavior. This study’s results demonstrate that the number of studies that address student behaviors on OSNs have recently increased. Moreover, the identified studies focused on five research streams, including academic purpose, cyber victimization, addiction, personality issues, and knowledge sharing behaviors. Most of these studies focused on the use and effect of OSNs on student academic performance. Most importantly, the proposed study framework provides a theoretical basis for further research in this context.

Introduction

The rapid development of Web 2.0 technologies has caused increased usage of online social networking (OSN) sites among individuals. OSNs such as Facebook are used almost every day by millions of users (Brailovskaia et al. 2020 ). OSNs allow individuals to present themselves via virtual communities, interact with their social networks, and maintain connections with others (Brailovskaia et al. 2020 ). Therefore, the use of OSNs has continually attracted young adults, especially students (Kokkinos and Saripanidis 2017 ; Paul et al. 2012 ). Given the popularity of OSNs and the increased number of students of different ages, many education institutions (e.g., universities) have used them to market their educational programs and to communicate with students (Paul et al. 2012 ). The popularity and ubiquity of OSNs have radically changed education systems and motivated students to engage in the educational process (Lambić 2016 ). The children of the twenty-first century are technology-oriented, and thus their learning style differs from previous generations (Moghavvemi et al. 2017a , b ). Students in this era have alternatives to how and where they spend time to learn. OSNs enable students to share knowledge and seek help from other students. Lim and Richardson ( 2016 ) emphasized that one important advantage of OSNs as an educational tool is to increase connections between classmates, which increases information sharing. Furthermore, the use of OSNs has also opened new communication channels between students and teachers. Previous studies have shown that students strengthened connections with their teachers and instructors using OSNs (e.g., Facebook, and Twitter). Therefore, the characteristics and features of OSNs have caused many students to use them as an educational tool, due to the various facilities provided by OSN platforms, which makes learning more fun to experience (Moghavvemi et al. 2017a ). This has caused many educational institutions to consider Facebook as a medium and as a learning tool for students to acquire knowledge (Ainin et al. 2015 ).

OSNs including Facebook, YouTube, and Twitter have been the most utilized platforms for education purposes (Akçayır and Akçayır 2016 ). For instance, the number of daily active users on Facebook reached 1.73 billion in the first quarter of 2020, with an increase of 11% compared to the previous year (Facebook 2020 ). As of the second quarter of 2020, Facebook has over 2.7 billion active monthly users (Clement 2020 ). Lim and Richardson ( 2016 ) empirically showed that students have positive perceptions toward using OSNs as an educational tool. A review of the literature shows that many studies have investigated student behaviors on these sites, which indicates the significance of the current review in providing an in-depth understanding of student behavior on OSNs. To date, various studies have investigated why students use OSNs and explored different student behaviors on these sites. Although there is an increasing amount of literature on this emerging topic, little research has been devoted to consolidating the current knowledge on OSN student behaviors. Moreover, to utilize the power of OSNs in an education context, it is important to study and understand student behaviors in this setting. However, current research that investigates student behaviors in OSNs is rather fragmented. Thus, it is difficult to derive in-depth and meaningful implications from these studies. Therefore, a systematic review of previous studies is needed to synthesize previous findings, identify gaps that need more research, and provide opportunities for further research. To this end, the purpose of this study is to explore the current literature in order to understand student behaviors in online social networks. Accordingly, a systematic review was conducted in order to collect, analyze, and synthesize current studies on student behaviors in OSNs.

This study drew on the Stimulus-Organism-Response (SOR) model to classify factors and develop a framework for better understanding of student behaviors in the context of OSNs. The S-O-R model suggests that various aspects of the environment (S), incite individual cognitive and affective reactions (O), which in turn derives their behavioral responses (R) (Mehrabian and Russell 1974 ). In order to achieve effective results in a clear and understandable manner, five research questions were proposed as shown below.

What was the research regional context covered in previous studies?

What were the focus and trends of previous studies?

What were the research methods used in previous studies?

What were the major theories adopted in previous studies?

What important factors were studied to understand student usage behaviors in OSNs?

This paper is organized as follows. The second section discusses the concept of online social networks and their definition. The third section describes the review method used to extract, analyze, and synthesize studies on student behaviors. The fourth section provides the result of analyzing the 104 identified primary studies and summarizes their findings based on the research questions. The fifth section provides a discussion on the results based on each research question. The sixth section highlights the limitations associated with this study, and the final section provides a conclusion of the study.

• Online social networks

Since online social networks such as Facebook were introduced last decade, they have attracted millions of users and have become integrated into our daily routines. OSNs provide users with virtual spaces where they can find other people with similar interests to communicate with and share their social activities (Lambić et al. 2016 ). The concept of OSNs is a combination of technology, information, and human interfaces that enable users to create an online community and build a social network of friends (Borrero et al. 2014 ). Kum Tang and Koh ( 2017 ) defined OSNs as “web-based virtual communities where users interact with real-life friends and meet other people with shared interests” . A more detailed and well-cited definition of OSN was introduced by Boyd and Ellison ( 2008 ) who defined OSNs as “web-based services that allow individuals to (1) construct a public or semipublic profile within a bounded system, (2) articulate a list of other users with whom they share a connection, and (3) view and traverse their list of connections and those made by others within the system” . Due to its popularity, many researches have examined the effect of OSNs on different disciplines such as business (Kujur and Singh 2017 ), healthcare (Chung 2014 ; Lin et al. 2016 ; Mano 2014 ), psychology (Pantic 2014 ), and education (Hamid et al.  2016 , 2015 ; Roblyer et al. 2010 ).

The heavy use of OSNs by students has led many studies to examine both positive and negative effects of these sites on students, including the time spent on OSNs usage (Chang and Heo 2014 ; Wohn and Larose 2014 ), engagement in academic activities (Ha et al. 2018 ; Sheeran and Cummings 2018 ), as well as the effect of OSN on students’ academic performance. Lim and Richardson ( 2016 ) stated that the main reasons for students to use OSNs as an educational tool is to increase their interactions and establish connections with classmates. Tower et al. ( 2014 ) found that OSN platforms such as Facebook have the potential to improve student self-efficacy in learning and develop their learning skills to a higher level. Therefore, some education institutions have started to develop their own OSN learning platforms (Tally 2010 ). Mazman and Usluel ( 2010 ) highlighted that using OSNs for educational and instructional contexts is an idea worth developing because students spend a lot of time on these platforms. Yet, the educational activities conducted on OSNs are dependent on the nature of the OSNs used by the students (Benson et al. 2015 ). Moreover, for teaching and learning, instructors have begun using OSNs platforms for several other purposes such as increasing knowledge exchanges and effective learning (Romero-Hall 2017 ). On the other hand, previous studies have raised some challenges of using OSNs for educational purposes. For example, students tend to use OSNs as a social tool for entraining rather than an educational tool (Baran 2010 ; Gettman and Cortijo 2015 ). Moreover, the active use of OSNs on daily basis may develop students’ negative behavior such as addiction and distraction. In this context, Kitsantas et al. ( 2016 ) found that college students in the United States reported some concerns such as the OSNs usage can turn into addictive behavior, distraction, privacy threats, the negative impact on their emotional health, and the inability to complete the tasks on time. Another challenge of using OSNs as educational tools is gender differences. Kim and Yoo ( 2016 ) found some differences between male and female students concerning the negative impact of OSNs, for example, female students are more conserved about issues related to security, and the difficulty of task/work completion. Furthermore, innovation is a key aspect in the education process (Serdyukov 2017 ), however, using OSNs as an educational tool, students could lose creativity due to the easy access to everything using these platforms (Mirabolghasemi et al. 2016 ).

Review method

This study employed a Systematic Literature Review (SLR) approach in order to answer the research questions. The SLR approach creates a foundation that advances knowledge and facilitates theory development for a specific topic (Webster and Watson 2002 ). Kitchenham and Charters ( 2007 ) defined SLR as a process of identifying, evaluating, and synthesizing all available research that is related to research questions, area of research, or new phenomenon. This study follows Kitchenhand and Charters’ guidelines (Kitchenham 2004 ), which state that the SLR approach involves three main stages: planning the review, conducting the review, and reporting the review results. There are several motivations for carrying out this systematic review. First, to summarize existing knowledge and evidence on research related to OSNs such as the theories, methods, and factors that influence student behaviors on these platforms. Second, to discover the current research focus and trends in this setting. Third, to propose a framework that classifies the factors that influence student behaviors on OSNs using the S-O-R model. The reasons for using S-O-R model in this study are twofold. First, S-O-R is a crucial theoretical framework to understand individuals’ behavior, and it has been extensively used in previous studies on consumer behavior (Wang and Chang 2013 ; Zhang et al. 2014 ; Zhang and Benyoucef 2016 ), and online users’ behavior (Islam et al. 2018 ; Luqman et al. 2017 ). Second, using the S-O-R model can provide a structured manner to understand the effect of the technological features of OSNs as environmental stimuli on individuals’ behavior (Luqman et al. 2017 ). Therefore, the application of the S-O-R model can provide a guide in the OSNs literature to better understand the potential stimulus and organism factors that drive a student’s behavioral responses in the context of OSNs. The SLR was guided by five research questions (see “ Introduction ” section), which provide an in-depth understanding of the research topic. The rationale and motivation beyond considering these questions are stated in Table 1 .

Stage one: Planning

Before conducting any SLR, it is necessary to clarify the goal and the objectives of the review (Kitchenham and Charters 2007 ). After identifying the review objectives and the research questions, in the planning stage, it is important to design the review protocol that will be used to conduct the review (Kitchenham and Charters 2007 ). Using a clear review protocol will help define criteria for selecting the literature source, database, and search keywords. Review protocol reduce research bias and specifies the research method used to perform a systematic review (Kitchenham and Charters 2007 ). Figure  1 shows the review protocol used for this study.

Review protocol

Stage two: Conducting the review

In this stage relevant literature was collected using a two-stage approach, which was followed by the removal of duplicated articles using Mendeley software. Finally, the researchers applied selection criteria to identify the most relevant articles to the current review. The details of each step of this stage are discussed below:

Literature identification and collection

This study used a two-stage approach (Webster and Watson 2002 ) to identify and collect relevant articles for review. In the first stage, this study conducted a systematic search to identify studies that address student behaviors and the use of online social networks using selected academic databases, including the Web of Science, Wiley Online Library ScienceDirect, Scopus, Emerald, and Springer. The choice of these academic databases is consistent with previous SLR studies (Ahmadi et al. 2018 ; Balaid et al. 2016 ; Busalim and Hussin 2016 ). Derived from the structure of this review and the research questions, these online databases were searched by focusing on title, abstract, and keywords. The search in these databases started in May 2019 using the specific keywords of “students’ behavior”, “online social networking”, “social networking sites”, and “Facebook”. This study performed several searches in each database using Boolean logic operators (i.e., AND and OR) to obtain a large number of published studies related to the review topic.

The results from this stage were 164 studies published between 2010 and 2018. In the second stage, important peer-reviewed journals were checked to ensure that all relevant articles were collected. We used the same keywords to search on information systems and education journals such as Computers in Human Behavior, International Journal of Information Management, Computers and Education, and Education and Information Technologies. These journals among the top peer-reviewed journals that publish topics related to students' behavior, education technologies, and OSNs. The result from both stages was 188 studies related to student behaviors in OSN. Table 2 presents the journals with more than two articles published in these areas.

Study selection

Following the identification of these studies, and after deleting duplicated studies, this study examined title, abstract, or the content of each study using three selection criteria: (1) a focus on student behavior; (2) an examination of the context of online social networks; (3) and a qualification as an empirical study. After applying these criteria, a total of 96 studies remained as primary studies for review. We further conducted a forward manual search on a reference list for the identified primary studies, through which an additional 8 studies were identified. A total of 104 studies were collected. As depicted in Fig.  2 , the frequency of published articles related to student behaviors in online social networks has gradually increased since 2010. In this regard, the highest number of articles were published in 2017. We can see that from 2010 to 2012 the number of published articles was relatively low and significant growth in published articles was seen from 2013 to 2017. This increase reveals that studying the behavior of students on different OSN platforms is increasingly attractive to researchers.

Timeline of publication

For further analysis, this study summarized the key topics covered during the review timeline. Figure  3 visualizes the development of OSNs studies over the years. Studies in the first three years (2010–2012) revolved around the use of OSNs by students and the benefits of using these platforms for educational purposes. The studies conducted between 2013 and 2015 mostly focused on the effect of using OSNs on student academic performance and achievement. In addition, in the same period, several studies examined important psychological issues associated with the use of OSNs such as anxiety, stress, and depression. In the years 2016 to 2018, OSNs studies were expanded to include cyber victimization behavior, OSN addiction behavior such as Facebook addiction, and how OSNs provide a collaborative platform that enables students to share information with their colleagues.

Evolution of OSNs studies over the years

Review results

To analyze the identified studies, this study guided its review using four research questions. Using research questions allows the researcher to synthesize findings from previous studies (Chan et al. 2017 ). The following subsection provides a detailed discussion of each of these research questions.

RQ1: What was the research regional context covered in previous studies?

As shown in Fig.  3 , most primary studies were conducted in the United States (n = 37), followed by Asia (n = 21) and Europe (n = 15). Relatively few studies were conducted in Australia, Africa, and the Middle East (n = 6 each), and only five studies were conducted in more than one country. Most of these empirical studies used university or college students to examine and validate the research models. Furthermore, many of these studies examined student behavior by considering Facebook as an online social network (n = 58) and a few studies examined student behavior on Microblogging platforms like Twitter (n = 7). The rest of the studies used multiple online social networks such as Instagram, YouTube, and Moodle (n = 31).

As shown in Fig.  4 , most of the reviewed studies are conducted in the United States (US). Furthermore, these studies considered Facebook as the main OSN platform. However, the focus on examining the usage behavior of Facebook in Western countries, particularly the US, is one of the challenges of Facebook research, because Facebook is used in many countries with 80% of its users are outside of the US (Peters et al. 2015 ).

Distribution of published studies by region

RQ2: What were the focus and trends of previous studies?

The results indicate that the identified primary studies for student behaviors on online social networks covered a wide spectrum of different research contexts. Further examination shows that there are five research streams in the literature.

The first research stream focused on using OSNs for academic purposes. The educational usage of OSNs relies on their purpose of use. OSNs can improve student engagement in a course and provide them with a sense of connection to their colleagues (Lambić 2016 ). However, the use of OSNs by students can affect their education as students can easily shift from using OSNs for educational to entertainment purposes. Thus, many studies under this stream focus on the effect of OSNs use on student academic performance. For instance, Lambić ( 2016 ) examined the effect of frequent Facebook use on the academic performance of university students. The results showed that students using Facebook as an educational tool to facilitate knowledge sharing and discussion positively impacted academic performance. Consistent with this result, Ainin et al. ( 2015 ) found that data from 1165 university students revealed a positive relationship between Facebook use and student academic performance. On the other hand, Paul et al. ( 2012 ) found that time spent on OSNs negative impacted student academic behavior. Moreover, the results statistically highlight that increased student attention spans resulted in increased time spent on OSNs, which eventually results in a negatively effect on academic performance. The results from Karpinski et al. ( 2013 ) showed that the effect of OSNs usage on student academic performance could differ from one country to another.

In summary, previous studies on the relationship between OSN use and academic performance show mixed results. From the reviewed studies, there were disparate results due to a few reasons. For example, recent studies found that multitasking plays an important role in determining the relationship between OSN usage and student academic performance. Karpinski et al. ( 2013 ) found a negative relationship between using social network sites (SNSs) and Grade Point Average (GPA) that was moderated by multitasking. Moreover, results from Junco ( 2015 ), illustrated that besides multitasking, student class rank is another determinant of the relationship between OSN platforms like Facebook and academic performance. The results revealed that senior students spent significantly less time on Facebook while doing schoolwork than freshman and sophomore students.

The second research stream is related to cyber victimization. Studies in this stream focused on negative interactions on OSNs like Facebook, which is the main platform where cyber victimization occurs (Kokkinos and Saripanidis 2017 ). Moreover, most studies in this stream examined the cyberbullying concept on OSNs. Cyberbullying is defined as “any behavior performed through electronic media by individuals or groups of individuals that repeatedly communicates hostile or aggressive messages intended to inflict harm or discomfort on others” (Tokunaga 2010 , p. 278). For instance, Gahagan et al. ( 2016 ) investigated the experiences of college students with cyberbullying on SNSs, and the results showed that 46% of the tested sample witnessed someone who had been bullied through the use of SNSs. Walker et al. ( 2011 ) conducted an exploratory study among undergraduate students to investigate their cyberbullying experiences. The results of the study highlighted that the majority of respondents knew someone who had been bullied on SNSs (Benson et al. 2015 ).

The third research stream focused on student addiction to OSNs use. Recent research has shown that excessive OSN use can lead to addictive behavior among students (Shettar et al. 2017 ). In this stream, Facebook was the main addictive ONS platform that was investigated (Shettar et al. 2017 ; Hong and Chiu 2016 ; Koc and Gulyagci 2013 ). Facebook addiction is defined as an excessive attachment to Facebook that interferes with daily activities and interpersonal relationships (Elphinston and Noller 2011 ). According to Andreassen et al. ( 2012 ), Facebook addiction has six general characteristics including salience, tolerance, mood modification, withdrawal, conflict, and relapse. As university students frequently have high levels of stress due to various commitments, such as assignment deadlines, exams, and high pressure to perform, they tend to use Facebook for mood modification (Brailovskaia and Margraf 2017 ; Brailovskaia et al. 2018 ). On further analysis, it was noticed that Facebook addiction among students was associated with other factors such as loneliness (Shettar et al. 2017 ), personality traits (i.e., openness agreeableness, conscientiousness, emotional stability, and extraversion) (Błachnio et al. 2017 ; Tang et al. 2016 ), and physical activities (Brailovskaia et al. 2018 ). Studies have examined student addiction behavior on different OSNs platforms. For instance, Ndasauka et al. ( 2016 ), empirically examined excessive Twitter use among college students. Kum Tang and Koh ( 2017 ) investigated the prevalence of different addiction behaviors (i.e., food and shopping addiction) and effective disorders among college students. In addition, a study by Chae and Kim (Chae et al. 2017 ) examined psychosocial differences in ONS addiction between female and male students. The results of the study showed that female students had a higher tendency towards OSNs addiction than male students.

The fourth stream of research highlighted in this review focused on student personality issues such as self-disclosure, stress, depression, loneliness, and self-presentation. For instance, Chen ( 2017 ) investigated the antecedents that predict positive student self-disclosure on SNSs. Tandoc et al. ( 2015 ) used social rank theory and Facebook envy to test the depression scale between college students. Skues et al. ( 2012 ) examined the relationship between three traits in the Big Five Traits model (neuroticism, extraversion, and openness) and student Facebook usage. Chang and Heo ( 2014 ) investigated the factors that explain the disclosure of a student’s personal information on Facebook.

The fifth reviewed research stream focused on student knowledge sharing behavior. For instance, Kim et al. ( 2015 ) identified the personal factors (self-efficacy) and environmental factors (strength of social ties and size of social networks) that affect information sharing behavior amongst university students. Eid and Al-Jabri ( 2016 ) examined the effect of various SNS characteristics (file sharing, chatting and online discussion, content creation, and enjoyment and entertainment) on knowledge sharing and student learning performance. Moghavvemi et al. ( 2017a , b ) examined the relationship between enjoyment, perceived status, outcome expectations, perceived benefits, and knowledge sharing behavior between students on Facebook. Figure  5 provides a mind map that shows an overview of the research focus and trends found in previous studies.

Reviewed studies research focus and trends

RQ3: What were the research methods used in previous studies?

As presented in Fig.  6 , previous studies used several research methods to examine student behavior on online social networks. Surveys were the method used most frequently in primary studies to understand the different types of determinants that effect student behaviors on online social networks, followed by the experiment method. Studies used the experiment method to examine the effect of online social networks content and features on student behavior, For example, Corbitt-Hall et al. ( 2016 ) had randomly assigned students to interact with simulated Facebook content that reflected various suicide risk levels. Singh ( 2017 ) used data mining techniques to collect student interaction data from different social networking sites such as Facebook and Twitter to classify student academic activities on these platforms. Studies that investigated student intentions, perceptions, and attitudes towards OSNs used survey data. For instance, Doleck et al. ( 2017 ) distributed an online survey to college students who used Facebook and found that perceived usefulness, attitude, and self-expression were influential factors towards the use of online social networks. Moreover, Ndasauka et al. ( 2016 ) used the survey method to assess the excessive use of Twitter among college students.

Research method distribution

RQ4: What were the major theories adopted in previous studies?

The results from the SLR show that previous studies used several theories to understand student behavior in online social networks. Table 3 depicts the theories used in these studies, with Use and Gratification Theory (UGT) being the most popular theory use to understand students' behaviors (Asiedu and Badu 2018 ; Chang and Heo 2014 ; Cheung et al. 2011 ; Hossain and Veenstra 2013 ). Furthermore, the social influence theory and the Big Five Traits model were applied in at least five studies each. The theoretical insights into student behaviors on online social networks provided by these theories are listed below:

Motivation aspect: since the advent of online social networks, many studies have been conducted to understand what motivates students to use online social networks. Theories such as UGT have been widely used to understand this issue. For example, Hossain and Veenstra ( 2013 ) conducted an empirical study to investigate what drives university students in the United States of America to use Social Networking Sites (SNSs) using the theoretical foundation of UGT. The study found that the geographic or physical displacement of students affects the use and gratification of SNSs. Zheng Wang et al. ( 2012a , b ) explained that students are motivated to use social media by their cognitive, emotional, social, and habitual needs as well as that all four categories significantly drive students to use social media.

Social-related aspect: Social theories such as Social Influence Theory, Social Learning Theory, and Social Capital Theory have also been used in several previous studies. Social Influence Theory determines what individual behaviors or opinions are affected by others. Venkatesh, Morris, Davis, and Davis (2003) defined social influence as “the degree to which an individual perceives that important others believe he or she should use a new system” . Cheung et al. ( 2011 ) applied Social Influence Theory to examine the effect of social influence factors (subjective norms, group norms, and social identity) on intentions to use online social networks. The empirical results from 182 students revealed that only Group Norms had a significant effect on student intentions to use OSNs. Other studies attempted to empirically examine the effect of other social theories. For instance, Liu and Brown ( 2014 ) adapted Social Capital Theory to investigate whether college students' self-disclosure on SNSs directly affected their social capital. Park et al. ( 2014a , b ) investigated the effect of using SNSs on university student learning outcomes using social learning theory.

Behavioral aspect: This study have noticed that the Theory of Planned Behavior (TPB), Theory of Reasoned Action (TRA), Technology Acceptance Model (TAM), Unified Theory of Acceptance, and Use of Technology (UTAUT) were also utilized as a theoretical foundation in a number of primary studies. These theories have been widely applied in the information systems (IS) field to provide insights into information technology adoption among individuals (Zhang and Benyoucef 2016 ). In the context of online social networks, there were empirical studies that adapted these theories to understand student usage behaviors towards online social networks such as Facebook. For example, Doleck et al. ( 2017 ) applied TAM to investigate college student usage intentions towards SNSs. Chang and Chen ( 2014 ) applied TRA and TPB to investigate why college students share their location on Facebook. In addition, a recent study used UTAUT to examine student perceptions towards using Facebook as an e-learning platform (Moghavvemi et al. 2017a , b ).

RQ5: What important factors were studied to understand student usage behaviors in OSNs?

Throughout the SLR, this study has been able to identify the potential factors that influence student behaviors in online social networks. Furthermore, to synthesize these factors and provide a comprehensive overview, this study proposed a framework based on the Stimulus-Organism-Response (S-O-R) model. The S-O-R model was developed in environmental psychology by Mehrabian and Russell ( 1974 ). According to Mehrabian and Russell ( 1974 ), environmental cues act as stimuli that can affect an individual’s internal cognitive and affective states, which subsequently influences their behavioral responses. To do so, this study extracted all the factors examined in 104 identified primary studies and classified them into three key concepts: stimulus, organism, and response. The details on the important factors of each component are presented below.

Online social networks stimulus

Stimulus factors are triggers that encourage or prompt students to use OSNs. Based on the SLR results, there are three stimulus dimensions: social stimulus, personal stimulus, and OSN characteristics. Social stimuli are cues embedded in the OSN that drive students to use these platforms. As shown in Fig.  7 , this study has identified six social stimulus factors including social support, social presence, social communication, social enhancement, social network size, and strength of social ties. Previous studies found that social aspects are a potential driver of student usage of OSNs. For instance, Kim et al. ( 2011 ) explored the motivation behind college student use of OSNs and found that seeking social support is one of the primary usage triggers. Lim and Richardson ( 2016 ) stated that using OSNs as educational tools will increase interactions and establish connections between students, which will enhance their social presence. Consistent with this, Cheung et al. ( 2011 ) found that social presence and social enhancement both have a positive effect on student use of OSNs. Other studies have tested the effect of other social factors such as social communication (Lee 2015 ), social network size, and strength of social ties (Chang and Heo 2014 ; Kim et al. 2015 ). Personal stimuli are student motivational factors associated with a specific state that affects their behavioral response. As depicted in Table 4 , researchers have tested different personal student needs that stimulate OSN usage. For instance, Zheng Wang et al. ( 2012a , b ) examined the emotional, social, and cognitive needs that drive students to use OSNs. Moghavvemi et al. ( 2017a , b ) empirically showed that students with a hedonic motivation were willing to use Facebook as an e-learning tool.

Classification framework for student behaviors in online social networks

OSN website characteristics are stimuli related to the cues implanted in an OSN website. In the reviewed studies, it was found that the most well studied OSN characteristics are usefulness and ease of use. Ease of use refers to student perceptions on the extent to which OSN are easy to use whereas usefulness refers to the degree that students believed that using OSN was helpful in enhancing their task performance (Arteaga Sánchez et al. 2014 ). Although student behaviors in OSNs have been widely studied, few studies have focused on OSN characteristics that stimulate student behaviors. For example, Eid and Al-Jabri ( 2016 ) examined the effect of OSN characteristics such as chatting, discussion, content creation, and file sharing. The results showed that file sharing, chatting, and discussion had a positive impact on student knowledge sharing behavior. In summary, Table 4 shows the stimulus factors identified in previous studies and their classification.

Online social networks organisms

Organism in this study’s framework refers to student internal evaluations towards using OSNs. There are four types of organism factors that have been highlighted in the literature. These types include personality traits, values, social, and cognitive reactions. Student personality traits influence the use of OSNs (Skues et al. 2012 ). As shown in Table 4 , self-esteem and self-disclosure were the most examined personality traits associated with student OSN behaviors. Self-esteem refers to an individual’s emotional evaluation of their own worth (Chen 2017 ). For example, Wang et al. ( 2012a , b ) examined the effect of the Big Five personality traits on student use of specific OSN features. The results found that students with high self-esteem were more likely to comment on other student profiles. Self-disclosure refers to the process by which individuals share their feelings, thoughts, information, and experiences with others (Dindia 1995 ). Previous studies have examined student self-disclosure in OSNs to explore information disclosure behavior (Chang and Heo 2014 ), location disclosure (Chang and Chen 2014 ), self-disclosure, and mental health (Zhang 2017 ). The second type of organism factors is value. It has been noticed that there are several value related factors that affect student internal organisms in OSNs. As shown in Table 4 , entertainment and enjoyment factors were the most common value examined in previous studies. Enjoyment is one of the potential drivers of student OSN use (Nawi et al. 2017 ). Eid and Al-Jabri ( 2016 ) found that YouTube is the most dominant OSN platform used by students for enjoyment and entertainment. Moreover, enjoyment and entertainment directly affected student learning performance.

Social organism refers to the internal social behavior of students that affect their use of OSNs. Students interact with OSN platforms when they experience positive social reactions. Previous studies have examined some social organism factors including relationship with faculty members, engagement, leisure activities, social skills, and chatting and discussion. The fourth type of organism factors is cognitive reactions. Parboteeah et al. ( 2009 ) defined cognitive reaction as “the mental process that occurs in an individual’s mind when he or she interacts with a stimulus” . The positive or negative cognitive reaction of students influences their responses towards OSNs. Table 5 presents the most common organism reactions that effect student use of OSNs.

Online social networks response

In this study’s framework, response refers to student reactions to OSNs stimuli and organisms. As shown in Table 5 , academic related behavior and negative behavior are the most common student responses towards OSNs. Studying the effect of OSN usage on student academic performance has been the most common research topic (Lambić 2016 ; Paul et al. 2012 ; Wohn and Larose 2014 ). On the other hand, other studies have examined the negative behavior of students during their usage of ONS, mostly towards ONS addiction (Hong and Chiu 2016 ; Shettar et al. 2017 ) or cyberbullying (Chen 2017 ; Gahagan et al. 2016 ). Table 6 summarizes student responses associated with OSNs use in previous studies.

Discussion and implications

The last two decades have witnessed a dramatic growth in the number of online social networks used among the youth generation. Examining student behaviors on OSN platforms has increasingly attracted scholars. However, there has been little effort to summarize and synthesize these findings. In this review study, a systematic literature review was conducted to synthesize previous research on student behaviors in OSNs to consolidate the factors that influence student behaviors into a classification framework using the S-O-R model. A total of 104 journal articles were identified through a rigorous and systematic search procedure. The collected studies from the literature show an increasing interest in the area ever since 2010. In line with the research questions, our analysis offers insightful results of the research landscape in terms of research regional context, studies focus trends, methodological trends, factors, and theories leveraged. Using the S-O-R model, we synthesized the reviewed studies highlighting the different stimuli, organism, and response factors. We synthesize and classify these factors into social stimuli, personal stimuli, and OSN characteristics, organism factors; personality traits, value, social, and cognitive reaction, and response; academic related behavior, negative behavior, and other responses.

Research regional perspective

The first research question focused on research regional context. The review revealed that most of the studies were conducted in the US followed by European countries, with the majority focusing on Facebook. The results show that the large majority of the studies were based on a single country. This indicates a sustainable research gap in examining the multi-cultural factors in multiple countries. As OSN is a common phenomenon across many counties, considering the culture and background differences can play an essential role in understanding students’ behavior on these platforms. For example, Ifinedo ( 2016 ) collected data from four countries in America (i.e., USA, Canada, Argentina, and Mexico) to understand students’ pervasive adoption of SNSs. The results from the study revealed that the individualism–collectivism culture factor has a positive impact on students' pervasive adoption behavior of SNSs, and the result reported high level of engagement from students who have more individualistic cultures. In the same manner, Kim et al. ( 2011 ) found some cultural differences in use of the SNSs platforms between Korean and US students. For example, considering the social nature of SNSs, the study found that Korean students rely more on online social relationships to obtain social support, where US students use SNSs to seek entertainment. Furthermore, Karpinski et al. ( 2013 ) empirically found significant differences between US and European students in terms of the moderating effect of multitasking on the relationship between SNS use and academic achievement of students. The confirms that culture issues may vary from one country to another, which consequently effect students’ behavior to use OSNs (Kim et al. 2011 ).

Studies focus and trends

The second research question of this review focused on undersigning the topics and trends that have been discussed in extant studies. The review revealed evidence of five categories of research streams based on the research focus and trend. As shown in Fig.  5 , most of the reviewed studies are in the first stream, which is using OSNs for academic purposes. Moreover, the trend of these studies in this stream focus on examining the effect of using OSNs on students’ academic performance and investigating the use of OSNs for educational purposes. However, a number of other trends are noteworthy. First, as cyber victimization is a relatively new concept, most of the studies provide rigorous effort in exporting the concept, and the reasons beyond its existence among students; however, we have noticed that no effort has been made to investigate the consequences of this negative behavior on students’ academic performance, social life, and communication. Second, we identified only two studies that examined the differences between undergraduate and postgraduate students in terms of cyber victimization. Therefore, there are many avenues for further research to untangle the demographic, education level, and cultural differences in this context. Third, our analysis revealed that Facebook was the most studied ONS platform in terms of addiction behavior, however, over the last ten years, the rapid growth of using image-based ONS such as Instagram and Pinterest has attracted many students (Alhabash and Ma 2017 ). For example, Instagram represents the fastest growing OSNs among young adult users age between 18 and 29 years old (Alhabash and Ma 2017 ). The overwhelming majority of the studies focus on Facebook users, and very few studies have examined excessive Instagram use (Kırcaburun and Griffiths 2018 ; Ponnusamy et al. 2020 ). Although OSNs have many similar features, each platform has unique features and a different structure. These differences in OSNs platforms urge further research to investigate and understand the factors related to excessive and addiction use by students (Kircaburun and Griffiths 2018 ). Therefore, based on the current research gaps, future research agenda including three topics/trend need to be considered. We have developed research questions for each topic as a direction for any further research as shown in Table 7 .

Theories and research methods

The third and fourth research questions focused on understanding the trends in terms of research methods and theories leveraged in existing studies. In relation to the third research question, the review highlighted evidence of the four research methods (i.e., survey, experiment, focus group/interview, and mix method) with a heavy focus on using a quantitative method with the majority of studies conducting survey. This may call for utilizing a variety of other research methods and research design to have more in-depth understanding of students’ behavior on OSN. For example, we noticed that few studies leveraged qualitative methods such as interviews and focus groups (n = 5). In addition, using mix method may derive more results and answer research questions that other methods cannot answer (Tashakkori and Teddlie 2003 ). Experimental methods have been used sparingly (n = 10), this may trigger an opportunity for more experimental research to test different strategies that can be used by education institutions to leverage the potential of OSN platforms in the education process. Moreover, considering that students’ attitude and behavior will change over time, applying longitudinal research method may offer opportunities to explore students’ attitude and behavior patterns over time.

The fourth research question focused on understanding the theoretical underpinnings of the reviewed studies. The analysis revealed two important insights; (1) a substantial number of the reviewed studies do not explicitly use an applied theory, and (2) out of the 34 studies that used theory, nine studies applied UGT to understand the motivation beyond using the OSN. Our findings categorized these theories under three aspects; motivational, social, and behavioral. While each aspect and theory offers useful lenses in this context, there is a lack of leveraging other theories in the extant literature. This motivates researchers to underpin their studies in theories that provide more insights into these three aspects. For example, majority of the studies have applied UGT to understand students’ motivate for using OSNs. However, using other motivational theories could uncover different factors that influence students' motivation for using OSNs. For example, self-determination theory (SDT) focuses on the extent to which individual’s behavior is self-motivated and determined. According to Ryan and Deci ( 2000 ), magnitude and types both shape individuals’ extrinsic motivation. The extrinsic motivation is a spectrum and depends on the level of self-determination (Wang et al. 2019 ). Therefore, the continuum aspect proposed by SDT can provide in-depth understanding of the extrinsic motivation. Wang et al. ( 2016 ) suggested that applying SDT can play a key role in understanding SNSs user satisfaction.

Another theoretical perspective that is worth further exploration relates to the psychological aspect. Our review results highlighted that a considerable number of studies focused on an important issue arising from the daily use of OSNs, such as excessive use/addiction (Koc and Gulyagci 2013 ; Shettar et al. 2017 ), Previous studies have investigated the behavior aspect beyond these issues, however, understanding the psychological aspect of Facebook addiction is worth further investigation. Ryan et al. ( 2014 ) reviewed Facebook addiction related studies and found that Facebook addiction is also linked to psychological factors such as depression and anxiety.

Factors that influence students behavior: S-O-R Framework

The fifth research question focused on determining the factors studies in the extant literature. The review analysis showed that stimuli factors included social, personal, and OSNs website stimuli. However, different types of stimuli have received less attention than other stimuli. Most studies leveraged the social and students’ personal stimuli. Furthermore, few studies conceptualized the OSNs websites characterises in terms of students beliefs about the effect of OSNs features and functions (e.g., perceived ease of use, user friendly) on students stimuli; it would be significant to develop a typology of the OSNs websites stimuli and systematically examine their effect on students’ attitude and behavior. We recommend applying different theories (as mentioned in Theories and research methods section) as an initial step to further identify stimuli factors. The results also highlight that cognitive reaction plays an essential role in the organism dimension. When students encounter stimuli, their internal evaluation is dominated by emotions. Therefore, the cognitive process takes place between students’ usage behavior and their responses (e.g., effort expectancy). In this review, we reported few studies that examined the effect of the cognitive reaction of students.

Response factors encompass students’ reaction to OSNs platforms stimuli and organism. Our review revealed an unsurprisingly dominant focus on the academic related behavior such as academic performance. While it is important to examine the effect of various stimuli and organism factors on academic related behavior and OSNs negative behavior, the psychological aspect beyond OSNs negative behavior is equallty important.

Limitations

Similar to other systematic review studies, this study has some limitations. The findings of our review are constrained by only empirical studies (journal articles) that meet the inclusion criteria. For instance, we only used the articles that explicitly examined students’ behavior in OSNs. Moreover, other different types of studies such as conference proceedings are not included in our primary studies. Further research efforts can gain additional knowledge and understanding from practitioner articles, books and, white papers. Our findings offer a comprehensive conceptual framework to understand students’ behavior in OSNs; future studies are recommended to perform a quantitative meta-analysis to this framework and test the relative effect of different stimuli factors.

Conclusions

The use of OSNs has become a daily habit among young adults and adolescents these days (Brailovskaia et al. 2020 ). In this review, we used a rigorous systematic review process and identified 104 studies related to students’ behavior in OSNs. We systematically reviewed these studies and provide an overview of the current state of this topic by uncovering the research context, research focus, theories, and research method. More importantly, we proposed a classification framework based on S-O-R model to consolidate the factors that influence students in online social networks. These factors were classified under different dimensions in each category of the S-O-R model; stimuli (Social Stimulus, Personal Stimulus, and OSN Characteristics), organism (Personality traits, value, social, Cognitive reaction), and students’ responses (academic-related behavior, negative behavior, and other responses). This framework provides the researchers with a classification of the factors that have been used in previous studies which can motivate further research on the factors that need more empirical examination (e.g., OSN characteristics).

Availability of data and materials

Not applicable.

Ahmadi, H., Arji, G., Shahmoradi, L., Safdari, R., Nilashi, M., & Alizadeh, M. (2018). The application of internet of things in healthcare: A systematic literature review and classification. Universal Access in the Information Society . https://doi.org/10.1007/s10209-018-0618-4 .

Article   Google Scholar  

Ainin, S., Naqshbandi, M. M., Moghavvemi, S., & Jaafar, N. I. (2015). Facebook usage, socialization and academic performance. Computers and Education, 83, 64–73. https://doi.org/10.1016/j.compedu.2014.12.018 .

Akbari, E., Naderi, A., Simons, R.-J., & Pilot, A. (2016). Student engagement and foreign language learning through online social networks. Asian-Pacific Journal of Second and Foreign Language Education, 1 (1), 4. https://doi.org/10.1186/s40862-016-0006-7 .

Akcaoglu, M., & Bowman, N. D. (2016). Using instructor-led Facebook groups to enhance students’ perceptions of course content. Computers in Human Behavior, 65, 582–590. https://doi.org/10.1016/j.chb.2016.05.029 .

Akçayır, G., & Akçayır, M. (2016). Research trends in social network sites’ educational use: A review of publications in all SSCI journals to 2015. Review of Education, 4 (3), 293–319.

Alhabash, S., & Ma, M. (2017). A tale of four platforms: Motivations and uses of Facebook, Twitter, Instagram, and Snapchat among college students. Social Media + Society, 3 (1), 205630511769154. https://doi.org/10.1177/2056305117691544 .

Amador, P., & Amador, J. (2014). Academic advising via Facebook: Examining student help seeking. Internet and Higher Education, 21, 9–16. https://doi.org/10.1016/j.iheduc.2013.10.003 .

Andreassen, C. S., Torsheim, T., Brunborg, G. S., & Pallesen, S. (2012). Development of a Facebook addiction scale. Psychological Reports, 110 (2), 501–517. https://doi.org/10.2466/02.09.18.PR0.110.2.501-517 .

Arteaga Sánchez, R., Cortijo, V., & Javed, U. (2014). Students’ perceptions of Facebook for academic purposes. Computers and Education, 70, 138–149. https://doi.org/10.1016/j.compedu.2013.08.012 .

Asiedu, N. K., & Badu, E. E. (2018). Motivating issues affecting students’ use of social media sites in Ghanaian tertiary institutions. Library Hi Tech, 36 (1), 167–179. https://doi.org/10.1108/LHT-10-2016-0108 .

Asterhan, C. S. C., & Bouton, E. (2017). Teenage peer-to-peer knowledge sharing through social network sites in secondary schools. Computers & Education, 110, 16–34. https://doi.org/10.1016/j.compedu.2017.03.007 .

Balaid, A., Abd Rozan, M. Z., Hikmi, S. N., & Memon, J. (2016). Knowledge maps: A systematic literature review and directions for future research. International Journal of Information Management, 36 (3), 451–475.

Baran, B. (2010). Facebook as a formal instructional environment. British Journal of Educational Technology, 41 (6), 146–149. https://doi.org/10.1111/j.1467-8535.2010.01115.x .

Benson, V., & Filippaios, F. (2015). Collaborative competencies in professional social networking: Are students short changed by curriculum in business education? Computers in Human Behavior, 51, 1331–1339. https://doi.org/10.1016/j.chb.2014.11.031 .

Benson, V., Saridakis, G., & Tennakoon, H. (2015). Purpose of social networking use and victimisation: Are there any differences between university students and those not in HE? Computers in Human Behavior, 51, 867–872. https://doi.org/10.1016/j.chb.2014.11.034 .

Błachnio, A., Przepiorka, A., Senol-Durak, E., Durak, M., & Sherstyuk, L. (2017). The role of personality traits in Facebook and Internet addictions: A study on Polish, Turkish, and Ukrainian samples. Computers in Human Behavior, 68, 269–275. https://doi.org/10.1016/j.chb.2016.11.037 .

Borrero, D. J., Yousafzai, Y. S., Javed, U., & Page, L. K. (2014). Perceived value of social networking sites (SNS) in students’ expressive participation in social movements. Journal of Research in Interactive Marketing, 8 (1), 56–78. https://doi.org/10.1108/JRIM-03-2013-0015 .

Borrero, J. D., Yousafzai, S. Y., Javed, U., & Page, K. L. (2014). Expressive participation in Internet social movements: Testing the moderating effect of technology readiness and sex on student SNS use. Computers in Human Behavior, 30, 39–49. https://doi.org/10.1016/j.chb.2013.07.032 .

Boyd, D. M., & Ellison, N. B. (2008). Social network sites: Definition, history, and scholarship. Journal of Computer-Mediated Communication, 13 (1), 210–230. https://doi.org/10.1111/j.1083-6101.2007.00393.x .

Brailovskaia, J., & Margraf, J. (2017). Facebook Addiction Disorder (FAD) among German students—A longitudinal approach. PLoS ONE, 12 (12), 1–15. https://doi.org/10.1371/journal.pone.0189719 .

Brailovskaia, J., Ströse, F., Schillack, H., & Margraf, J. (2020). Less Facebook use—More well-being and a healthier lifestyle? An experimental intervention study. Computers in Human Behavior, 108 (March), 106332. https://doi.org/10.1016/j.chb.2020.106332 .

Brailovskaia, J., Teismann, T., & Teismann, T. (2018). Physical activity mediates the association between daily stress and Facebook Addiction Disorder. Computers in Human Behavior, 86, 199–204. https://doi.org/10.1016/j.chb.2018.04.045 .

Busalim, A. H., & Hussin, A. R. C. (2016). Understanding social commerce: A systematic literature review and directions for further research. International Journal of Information Management, 36 (6), 1075–1088. https://doi.org/10.1016/j.ijinfomgt.2016.06.005 .

Cain, J., Scott, D. R., Tiemeier, A. M., Akers, P., & Metzger, A. H. (2013). Social media use by pharmacy faculty: Student friending, e-professionalism, and professional use. Currents in Pharmacy Teaching and Learning, 5 (1), 2–8. https://doi.org/10.1016/j.cptl.2012.09.002 .

Chae, D., Kim, H., & Kim, Y. A. (2017). Sex differences in the factors influencing Korean College Students’ addictive tendency toward social networking sites. International Journal of Mental Health and Addiction . https://doi.org/10.1007/s11469-017-9778-3 .

Chan, T. K. H., Cheung, C. M. K., & Lee, Z. W. Y. (2017). The state of online impulse-buying research: A literature analysis. Information & Management, 54 (2), 204–217. https://doi.org/10.1016/j.im.2016.06.001 .

Chang, C. W., & Chen, G. M. (2014). College students’ disclosure of location-related information on Facebook. Computers in Human Behavior, 35, 33–38. https://doi.org/10.1016/j.chb.2014.02.028 .

Chang, C. W., & Heo, J. (2014). Visiting theories that predict college students’ self-disclosure on Facebook. Computers in Human Behavior, 30, 79–86. https://doi.org/10.1016/j.chb.2013.07.059 .

Chen, B., & Marcus, J. (2012). Students’ self-presentation on Facebook: An examination of personality and self-construal factors. Computers in Human Behavior, 28 (6), 2091–2099. https://doi.org/10.1016/j.chb.2012.06.013 .

Chen, H. (2017). Antecedents of positive self-disclosure online: An empirical study of US college students Facebook usage. Psychology Research and Behavior Management, 10, 147–153. https://doi.org/10.2147/PRBM.S136049 .

Cheung, C. M. K., Chiu, P. Y., & Lee, M. K. O. (2011). Online social networks: Why do students use facebook? Computers in Human Behavior, 27 (4), 1337–1343.

Chung, J. E. (2014). Social networking in online support groups for health: How online social networking benefits patients. Journal of Health Communication, 19 (6), 639–659. https://doi.org/10.1080/10810730.2012.757396 .

Čičević, S., Samčović, A., & Nešić, M. (2016). Exploring college students’ generational differences in Facebook usage. Computers in Human Behavior, 56, 83–92. https://doi.org/10.1016/j.chb.2015.11.034 .

Clement, J. (2020). Facebook: Number of monthly active users worldwide 2008–2020 Published by J. Clement, Aug 10, 2020 How many users does Facebook have? With over 2.7 billion monthly active users as of the second quarter of 2020, Facebook is the biggest social network world . Retrieved from https://www.statista.com/statistics/264810/number-of-monthly-active-facebook-users-worldwide/

Corbitt-Hall, D. J., Gauthier, J. M., Davis, M. T., & Witte, T. K. (2016). College Students’ responses to suicidal content on social networking sites: An examination using a simulated facebook newsfeed. Suicide and Life-Threatening Behavior, 46 (5), 609–624. https://doi.org/10.1111/sltb.12241 .

Deng, L., & Tavares, N. J. (2013). From Moodle to Facebook: Exploring students’ motivation and experiences in online communities. Computers & Education, 68, 167–176. https://doi.org/10.1016/j.compedu.2013.04.028 .

Dindia, K. (1995). Self-disclosure: A sense of balance. Contemporary psychology: A journal of reviews . Vol. 40. New York: Sage Publications. https://doi.org/10.1037/003319 .

Book   Google Scholar  

Doleck, T., Bazelais, P., & Lemay, D. J. (2017). Examining the antecedents of social networking sites use among CEGEP students. Education and Information Technologies, 22 (5), 2103–2123. https://doi.org/10.1007/s10639-016-9535-4 .

Eid, M. I. M., & Al-Jabri, I. M. (2016). Social networking, knowledge sharing, and student learning: The case of university students. Computers and Education, 99, 14–27. https://doi.org/10.1016/j.compedu.2016.04.007 .

Elphinston, R. A., & Noller, P. (2011). Time to Face It! Facebook intrusion and the implications for romantic jealousy and relationship satisfaction. Cyberpsychology, Behavior, and Social Networking, 14 (11), 631–635. https://doi.org/10.1089/cyber.2010.0318 .

Enskat, A., Hunt, S. K., & Hooker, J. F. (2017). A generational examination of instructional Facebook use and the effects on perceived instructor immediacy, credibility and student affective learning. Technology, Pedagogy and Education, 26 (5), 545–557. https://doi.org/10.1080/1475939X.2017.1354065 .

Facebook. (2020). Facebook Reports First Quarter 2020 Results . Retrieved from http://investor.fb.com/releasedetail.cfm?ReleaseID=842071 .

Fasae, J. K., & Adegbilero-Iwari, I. (2016). Use of social media by science students in public universities in Southwest Nigeria. The Electronic Library, 34 (2), 213–222. https://doi.org/10.1108/EL-11-2014-0205 .

Gahagan, K., Vaterlaus, J. M., & Frost, L. R. (2016). College student cyberbullying on social networking sites: Conceptualization, prevalence, and perceived bystander responsibility. Computers in Human Behavior, 55, 1097–1105. https://doi.org/10.1016/j.chb.2015.11.019 .

George, D. R., Dellasega, C., Whitehead, M. M., & Bordon, A. (2013). Facebook-based stress management resources for first-year medical students: A multi-method evaluation. Computers in Human Behavior, 29 (3), 559–562. https://doi.org/10.1016/j.chb.2012.12.008 .

Gettman, H. J., & Cortijo, V. (2015). “Leave Me and My Facebook Alone!” understanding college students’ relationship with Facebook and its use for academic purposes. International Journal for the Scholarship of Teaching and Learning Article, 9 (1), 1. https://doi.org/10.20429/ijsotl.2015.090108 .

Ha, L., Joa, C. Y., Gabay, I., & Kim, K. (2018). Does college students’ social media use affect school e-mail avoidance and campus involvement? Internet Research, 28 (1), 213–231. https://doi.org/10.1108/IntR-11-2016-0346 .

Hamid, S., Bukhari, S., Ravana, S. D., Norman, A. A., & Ijab, M. T. (2016). Role of social media in information-seeking behaviour of international students: A systematic literature review. Aslib Journal of Information Management, 68 (5), 643–666. https://doi.org/10.1108/AJIM-03-2016-0031 .

Hamid, S., Waycott, J., Kurnia, S., & Chang, S. (2015). Understanding students’ perceptions of the benefits of online social networking use for teaching and learning. Internet and Higher Education, 26, 1–9. https://doi.org/10.1016/j.iheduc.2015.02.004 .

Hong, F. Y., & Chiu, S. L. (2016). Factors influencing facebook usage and facebook addictive tendency in university students: The role of online psychological privacy and facebook usage motivation. Stress and Health, 32 (2), 117–127. https://doi.org/10.1002/smi.2585 .

Hossain, M. D., & Veenstra, A. S. (2013). Online maintenance of life domains: Uses of social network sites during graduate education among the US and international students. Computers in Human Behavior, 29 (6), 2697–2702. https://doi.org/10.1016/j.chb.2013.07.007 .

Ifinedo, P. (2016). Applying uses and gratifications theory and social influence processes to understand students’ pervasive adoption of social networking sites: Perspectives from the Americas. International Journal of Information Management, 36 (2), 192–206. https://doi.org/10.1016/j.ijinfomgt.2015.11.007 .

Islam, T., Sheikh, Z., Hameed, Z., Khan, I. U., & Azam, R. I. (2018). Social comparison, materialism, and compulsive buying based on stimulus-response- model: A comparative study among adolescents and young adults. Young Consumers, 19 (1), 19–37. https://doi.org/10.1108/MRR-09-2015-0216 .

Josefsson, P., Hrastinski, S., Pargman, D., & Pargman, T. C. (2016). The student, the private and the professional role: Students’ social media use. Education and Information Technologies, 21 (6), 1583–1594. https://doi.org/10.1007/s10639-015-9403-7 .

Junco, R. (2012). The relationship between frequency of Facebook use, participation in Facebook activities, and student engagement. Computers and Education, 58 (1), 162–171. https://doi.org/10.1016/j.compedu.2011.08.004 .

Junco, R. (2015). Student class standing, Facebook use, and academic performance. Journal of Applied Developmental Psychology, 36, 18–29. https://doi.org/10.1016/j.appdev.2014.11.001 .

Karpinski, A. C., Kirschner, P. A., Ozer, I., Mellott, J. A., & Ochwo, P. (2013). An exploration of social networking site use, multitasking, and academic performance among United States and European university students. Computers in Human Behavior, 29 (3), 1182–1192. https://doi.org/10.1016/j.chb.2012.10.011 .

Kim, J., Lee, C., & Elias, T. (2015). Factors affecting information sharing in social networking sites amongst university students. Online Information Review, 39 (3), 290–309. https://doi.org/10.1108/OIR-01-2015-0022 .

Kim, S., & Yoo, S. J. (2016). Age and gender differences in social networking: effects on south Korean students in higher education. In Social networking and education (pp. 69–82). Switzerland: Springer. https://doi.org/10.1007/978-3-319-17716-8_5 .

Kim, Y., Sohn, D., & Choi, S. M. (2011). Cultural difference in motivations for using social network sites: A comparative study of American and Korean college students. Computers in Human Behavior, 27 (1), 365–372. https://doi.org/10.1016/j.chb.2010.08.015 .

Kircaburun, K., & Griffiths, M. D. (2018). Instagram addiction and the Big Five of personality: The mediating role of self-liking. Journal of Behavioral Addictions, 7 (1), 158–170. https://doi.org/10.1556/2006.7.2018.15 .

Kırcaburun, K., & Griffiths, M. D. (2018). Problematic Instagram use: The role of perceived feeling of presence and escapism. International Journal of Mental Health and Addiction . https://doi.org/10.1007/s11469-018-9895-7 .

Kitchenham, B. (2004). Procedures for performing systematic reviews. Keele University, UK and National ICT Australia, 33, 28.

Google Scholar  

Kitchenham, B., & Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering. Keele University and University of Durham, 2, 1051.

Kitsantas, A., Dabbagh, N., Chirinos, D. S., & Fake, H. (2016). College students’ perceptions of positive and negative effects of social networking. In Social networking and education (pp. 225–238). Switzerland: Springer, Cham. https://doi.org/10.1007/978-3-319-17716-8_14 .

Koc, M., & Gulyagci, S. (2013). Facebook addiction among Turkish College students: The role of psychological health, demographic, and usage characteristics. Cyberpsychology, Behavior, and Social Networking, 16 (4), 279–284. https://doi.org/10.1089/cyber.2012.0249 .

Kokkinos, C. M., & Saripanidis, I. (2017). A lifestyle exposure perspective of victimization through Facebook among university students. Do individual differences matter? Computers in Human Behavior, 74, 235–245. https://doi.org/10.1016/j.chb.2017.04.036 .

Krasilnikov, A., & Smirnova, A. (2017). Online social adaptation of first-year students and their academic performance. Computers and Education, 113, 327–338. https://doi.org/10.1016/j.compedu.2017.05.012 .

Kujur, F., & Singh, S. (2017). Engaging customers through online participation in social networking sites. Asia Pacific Management Review, 22 (1), 16–24. https://doi.org/10.1016/j.apmrv.2016.10.006 .

Kumar Bhatt, R., & Kumar, A. (2014). Student opinion on the use of social networking tools by libraries. The Electronic Library, 32 (5), 594–602. https://doi.org/10.1108/EL-09-2012-0110 .

Kuo, T., & Tang, H. L. (2014). Relationships among personality traits, Facebook usages, and leisure activities—A case of Taiwanese college students. Computers in Human Behavior, 31 (1), 13–19. https://doi.org/10.1016/j.chb.2013.10.019 .

Lambić, D. (2016). Correlation between Facebook use for educational purposes and academic performance of students. Computers in Human Behavior, 61, 313–320. https://doi.org/10.1016/j.chb.2016.03.052 .

Lee, S. (2015). Analyzing negative SNS behaviors of elementary and middle school students in Korea. Computers in Human Behavior, 43, 15–27. https://doi.org/10.1016/j.chb.2014.10.014 .

Lim, J., & Richardson, J. C. (2016). Exploring the effects of students’ social networking experience on social presence and perceptions of using SNSs for educational purposes. The Internet and Higher Education, 29, 31–39. https://doi.org/10.1016/j.iheduc.2015.12.001 .

Lin, W.-Y., Zhang, X., Song, H., & Omori, K. (2016). Health information seeking in the Web 2.0 age: Trust in social media, uncertainty reduction, and self-disclosure. Computers in Human Behavior, 56, 289–294. https://doi.org/10.1016/j.chb.2015.11.055 .

Liu, C. C., Chen, Y. C., & Diana Tai, S. J. (2017). A social network analysis on elementary student engagement in the networked creation community. Computers and Education, 115 (300), 114–125. https://doi.org/10.1016/j.compedu.2017.08.002 .

Liu, D., & Brown, B. B. (2014). Self-disclosure on social networking sites, positive feedback, and social capital among Chinese college students. Computers in Human Behavior, 38, 213–219. https://doi.org/10.1016/j.chb.2014.06.003 .

Luqman, A., Cao, X., Ali, A., Masood, A., & Yu, L. (2017). Empirical investigation of Facebook discontinues usage intentions based on SOR paradigm. Computers in Human Behavior, 70, 544–555. https://doi.org/10.1016/j.chb.2017.01.020 .

Mano, R. S. (2014). Social media and online health services: A health empowerment perspective to online health information. Computers in Human Behavior, 39, 404–412. https://doi.org/10.1016/j.chb.2014.07.032 .

Mazman, S. G., & Usluel, Y. K. (2010). Modeling educational usage of Facebook. Computers and Education, 55 (2), 444–453. https://doi.org/10.1016/j.compedu.2010.02.008 .

Mehrabian, A., & Russell, J. A. (1974). An approach to environmental psychology . Cambridge: The MIT Press.

Meier, A., Reinecke, L., & Meltzer, C. E. (2016). Facebocrastination? Predictors of using Facebook for procrastination and its effects on students’ well-being. Computers in Human Behavior, 64, 65–76. https://doi.org/10.1016/j.chb.2016.06.011 .

Mirabolghasemi, M., Iahad, N. A., & Rahim, N. Z. A. (2016). Students’ perception towards the potential and barriers of social network sites in higher education. In Social networking and education (pp. 41–49). Switzerland: Springer. https://doi.org/10.1007/978-3-319-17716-8_3 .

Moghavvemi, S., Paramanathan, T., Rahin, N. M., & Sharabati, M. (2017). Student’s perceptions towards using e-learning via Facebook. Behaviour and Information Technology, 36 (10), 1081–1100. https://doi.org/10.1080/0144929X.2017.1347201 .

Moghavvemi, S., Sharabati, M., Paramanathan, T., & Rahin, N. M. (2017). The impact of perceived enjoyment, perceived reciprocal benefits and knowledge power on students’ knowledge sharing through Facebook. International Journal of Management Education, 15 (1), 1–12. https://doi.org/10.1016/j.ijme.2016.11.002 .

Mostafa, R. B. (2015). Engaging students via social media: Is it worth the effort? Journal of Marketing Education, 37 (3), 144–159. https://doi.org/10.1177/0273475315585825 .

Nawi, N. B. C., Al Mamun, A., Nasir, N. A. B. M., Shokery, N. B., Raston, N. B. A., & Fazal, S. A. (2017). Acceptance and usage of social media as a platform among student entrepreneurs. Journal of Small Business and Enterprise Development, 24 (2), 375–393. https://doi.org/10.1108/JSBED-09-2016-0136 .

Ndasauka, Y., Hou, J., Wang, Y., Yang, L., Yang, Z., Ye, Z., et al. (2016). Excessive use of Twitter among college students in the UK: Validation of the Microblog Excessive Use Scale and relationship to social interaction and loneliness. Computers in Human Behavior, 55, 963–971. https://doi.org/10.1016/j.chb.2015.10.020 .

Nwagwu, W. E. (2017). Social networking, identity and sexual behaviour of undergraduate students in Nigerian universities. The Electronic Library, 35 (3), 534–558. https://doi.org/10.1108/EL-01-2015-0014 .

Pantic, I. (2014). Online social networking and mental health. Cyberpsychology, Behavior, and Social Networking, 17 (10), 652–657. https://doi.org/10.1089/cyber.2014.0070 .

Parboteeah, D. V., Valacich, J. S., & Wells, J. D. (2009). The influence of website characteristics on a consumer’s urge to buy impulsively. Information Systems Research, 20 (1), 60–78. https://doi.org/10.1287/isre.1070.0157 .

Park, N., Song, H., & Lee, K. M. (2014). Social networking sites and other media use, acculturation stress, and psychological well-being among East Asian college students in the United States. Computers in Human Behavior, 36, 138–146. https://doi.org/10.1016/j.chb.2014.03.037 .

Park, S. Y., Cha, S.-B., Lim, K., & Jung, S.-H. (2014). The relationship between university student learning outcomes and participation in social network services, social acceptance and attitude towards school life. British Journal of Educational Technology, 45 (1), 97–111. https://doi.org/10.1111/bjet.12013 .

Paul, J. A., Baker, H. M., & Cochran, J. D. (2012). Effect of online social networking on student academic performance. Computers in Human Behavior, 28 (6), 2117–2127. https://doi.org/10.1016/j.chb.2012.06.016 .

Peters, A. N., Winschiers-Theophilus, H., & Mennecke, B. E. (2015). Cultural influences on Facebook practices: A comparative study of college students in Namibia and the United States. Computers in Human Behavior, 49, 259–271. https://doi.org/10.1016/j.chb.2015.02.065 .

Ponnusamy, S., Iranmanesh, M., Foroughi, B., & Hyun, S. S. (2020). Drivers and outcomes of Instagram addiction: Psychological well-being as moderator. Computers in Human Behavior, 107, 106294. https://doi.org/10.1016/j.chb.2020.106294 .

Rap, S., & Blonder, R. (2017). Thou shall not try to speak in the Facebook language: Students’ perspectives regarding using Facebook for chemistry learning. Computers and Education, 114, 69–78. https://doi.org/10.1016/j.compedu.2017.06.014 .

Raymond, J., & Wang, H. (2015). Social network sites and international students ’ cross-cultural adaptation. Computers in Human Behavior, 49, 400–411. https://doi.org/10.1016/j.chb.2015.03.041 .

Roblyer, M. D., McDaniel, M., Webb, M., Herman, J., & Witty, J. V. (2010). Findings on Facebook in higher education: A comparison of college faculty and student uses and perceptions of social networking sites. Internet and Higher Education, 13 (3), 134–140. https://doi.org/10.1016/j.iheduc.2010.03.002 .

Romero-Hall, E. (2017). Posting, sharing, networking, and connecting: Use of social media content by graduate students. TechTrends, 61 (6), 580–588. https://doi.org/10.1007/s11528-017-0173-5 .

Rui, J. R., & Wang, H. (2015). Social network sites and international students’ cross-cultural adaptation. Computers in Human Behavior, 49, 400–411. https://doi.org/10.1016/j.chb.2015.03.041 .

Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation. Social Development, and Well-Being, 55 (1), 68–78.

Ryan, T., Chester, A., Reece, J., & Xenos, S. (2014). The uses and abuses of Facebook: A review of Facebook addiction. Journal of Behavioral Addictions, 3 (3), 133–148. https://doi.org/10.1556/JBA.3.2014.016 .

Serdyukov, P. (2017). Innovation in education: What works, what doesn’t, and what to do about it? Journal of Research in Innovative Teaching & Learning, 10 (1), 4–33. https://doi.org/10.1108/jrit-10-2016-0007 .

Sheeran, N., & Cummings, D. J. (2018). An examination of the relationship between Facebook groups attached to university courses and student engagement. Higher Education, 76, 937–955.

Shettar, M., Karkal, R., Kakunje, A., Mendonsa, R. D., & Chandran, V. V. M. (2017). Facebook addiction and loneliness in the post-graduate students of a university in southern India. International Journal of Social Psychiatry, 63 (4), 325–329. https://doi.org/10.1177/0020764017705895 .

Shim, M., Lee-Won, R. J., & Park, S. H. (2016). The self on the Net: The joint effect of self-construal and public self-consciousness on positive self-presentation in online social networking among South Korean college students. Computers in Human Behavior, 63, 530–539. https://doi.org/10.1016/j.chb.2016.05.054 .

Sin, S. C. J., & Kim, K. S. (2013). International students’ everyday life information seeking: The informational value of social networking sites. Library and Information Science Research, 35 (2), 107–116. https://doi.org/10.1016/j.lisr.2012.11.006 .

Singh, A. (2017). Mining of social media data of University students. Education and Information Technologies, 22 (4), 1515–1526. https://doi.org/10.1007/s10639-016-9501-1 .

Skues, J. L., Williams, B., & Wise, L. (2012). The effects of personality traits, self-esteem, loneliness, and narcissism on Facebook use among university students. Computers in Human Behavior, 28 (6), 2414–2419. https://doi.org/10.1016/j.chb.2012.07.012 .

Smith, R., Morgan, J., & Monks, C. (2017). Students’ perceptions of the effect of social media ostracism on wellbeing. Computers in Human Behavior, 68, 276–285. https://doi.org/10.1016/j.chb.2016.11.041 .

Special, W. P., & Li-Barber, K. T. (2012). Self-disclosure and student satisfaction with Facebook. Computers in Human Behavior, 28 (2), 624–630. https://doi.org/10.1016/j.chb.2011.11.008 .

Tally, S. (2010). Mixable blends Facebook with academics to improve student success . Purdue: Purdue University News.

Tandoc, E. C., Ferrucci, P., & Duffy, M. (2015). Facebook use, envy, and depression among college students: Is facebooking depressing? Computers in Human Behavior, 43, 139–146. https://doi.org/10.1016/j.chb.2014.10.053 .

Tang, C. S. K., & Koh, Y. Y. W. (2017). Online social networking addiction among college students in Singapore: Comorbidity with behavioral addiction and affective disorder. Asian Journal of Psychiatry, 25, 175–178. https://doi.org/10.1016/j.ajp.2016.10.027 .

Tang, J. H., Chen, M. C., Yang, C. Y., Chung, T. Y., & Lee, Y. A. (2016). Personality traits, interpersonal relationships, online social support, and Facebook addiction. Telematics and Informatics, 33 (1), 102–108. https://doi.org/10.1016/j.tele.2015.06.003 .

Tashakkori, A., & Teddlie, C. (2003). Issues and dilemmas in teaching research methods courses in social and behavioural sciences: US perspective. International Journal of Social Research Methodology: Theory and Practice, 6 (1), 61–77. https://doi.org/10.1080/13645570305055 .

Teo, T., Doleck, T., & Bazelais, P. (2017). The role of attachment in Facebook usage: a study of Canadian college students. Interactive Learning Environments, 4820 (April), 1–17. https://doi.org/10.1080/10494820.2017.1315602 .

Tokunaga, R. S. (2010). Following you home from school: A critical review and synthesis of research on cyberbullying victimization. Computers in Human Behavior . https://doi.org/10.1016/j.chb.2009.11.014 .

Tower, M., Latimer, S., & Hewitt, J. (2014). Social networking as a learning tool: Nursing students’ perception of efficacy. Nurse Education Today, 34 (6), 1012–1017. https://doi.org/10.1016/j.nedt.2013.11.006 .

Van Hoof, J. J., Bekkers, J., & Van Vuuren, M. (2014). Son, you’re smoking on Facebook! College students’ disclosures on social networking sites as indicators of real-life risk behaviors. Computers in Human Behavior, 34, 249–257. https://doi.org/10.1016/j.chb.2014.02.008 .

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27 (3), 425–478. https://doi.org/10.2307/30036540 .

Walker, C. M., Sockman, B. R., & Koehn, S. (2011). An exploratory study of cyberbullying with undergraduate students. Tech Trends, 55 (2), 31–38. https://doi.org/10.1007/s11528-011-0481-0 .

Wang, J. C., & Chang, C. H. (2013). How online social ties and product-related risks influence purchase intentions: A Facebook experiment. Electronic Commerce Research and Applications, 12 (5), 337–346.

Wang, J. L., Jackson, L. A., Gaskin, J., & Wang, H. Z. (2014). The effects of Social Networking Site (SNS) use on college students’ friendship and well-being. Computers in Human Behavior, 37, 229–236. https://doi.org/10.1016/j.chb.2014.04.051 .

Wang, J. L., Jackson, L. A., Zhang, D. J., & Su, Z. Q. (2012a). The relationships among the Big Five Personality factors, self-esteem, narcissism, and sensation-seeking to Chinese University students’ uses of social networking sites (SNSs). Computers in Human Behavior, 28 (6), 2313–2319. https://doi.org/10.1016/j.chb.2012.07.001 .

Wang, X., Li, Y., & Wang, X. (2016). Users’ satisfaction with social network sites: A self-determination perspective. Journal of Computer Information Systems , 4417 (February). https://doi.org/10.1080/08874417.2015.11645800

Wang, X., Lin, X., & Spencer, M. K. (2019). Exploring the effects of extrinsic motivation on consumer behaviors in social commerce: Revealing consumers’ perceptions of social commerce benefits. International Journal of Information Management, 45 (March 2018), 163–175. https://doi.org/10.1016/j.ijinfomgt.2018.11.010 .

Wang, Z., Tchernev, J. M., & Solloway, T. (2012b). A dynamic longitudinal examination of social media use, needs, and gratifications among college students. Computers in Human Behavior, 28 (5), 1829–1839. https://doi.org/10.1016/j.chb.2012.05.001 .

Webster, J., & Watson, R. (2002). Analyzing the past to prepare for the future: Writing a literature review. MIS Quarterly, 26 (2), 13–23.

Wickramanayake, L., & Muhammad Jika, S. (2018). Social media use by undergraduate students of education in Nigeria: A survey. The Electronic Library, 36 (1), 21–37. https://doi.org/10.1108/EL-01-2017-0023 .

Wodzicki, K., Schwämmlein, E., & Moskaliuk, J. (2012). “Actually, I Wanted to Learn”: Study-related knowledge exchange on social networking sites. Internet and Higher Education, 15 (1), 9–14. https://doi.org/10.1016/j.iheduc.2011.05.008 .

Wohn, D. Y., & Larose, R. (2014). Effects of loneliness and differential usage of Facebook on college adjustment of first-year students. Computers and Education, 76, 158–167. https://doi.org/10.1016/j.compedu.2014.03.018 .

Yazdanparast, A., Joseph, M., & Qureshi, A. (2015). An investigation of Facebook boredom phenomenon among college students. Young Consumers, 16 (4), 468–480. https://doi.org/10.1108/YC-02-2015-00506 .

Zhang, H., Lu, Y., Gupta, S., & Zhao, L. (2014). What motivates customers to participate in social commerce? The impact of technological environments and virtual customer experiences. Information Management, 51, 1017–1030.

Zhang, K. Z., & Benyoucef, M. (2016). Consumer behavior in social commerce: A literature review. Decision Support Systems, 86, 95–108.

Zhang, R. (2017). The stress-buffering effect of self-disclosure on Facebook: An examination of stressful life events, social support, and mental health among college students. Computers in Human Behavior, 75, 527–537. https://doi.org/10.1016/j.chb.2017.05.043 .

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This paper is supported by Fundamental Research Grant Scheme (FRGS) (Vote No. R.K130000.7840.4F245), and UTM Razak School of Engineering and Advanced Technology research grant or DPUTMRAZAK (Vote No. R.K13000.7740.4J313).

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Masrom, M.B., Busalim, A.H., Abuhassna, H. et al. Understanding students’ behavior in online social networks: a systematic literature review. Int J Educ Technol High Educ 18 , 6 (2021). https://doi.org/10.1186/s41239-021-00240-7

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Social Media Use and Its Connection to Mental Health: A Systematic Review

Fazida karim.

1 Psychology, California Institute of Behavioral Neurosciences and Psychology, Fairfield, USA

2 Business & Management, University Sultan Zainal Abidin, Terengganu, MYS

Azeezat A Oyewande

3 Family Medicine, California Institute of Behavioral Neurosciences and Psychology, Fairfield, USA

4 Family Medicine, Lagos State Health Service Commission/Alimosho General Hospital, Lagos, NGA

Lamis F Abdalla

5 Internal Medicine, California Institute of Behavioral Neurosciences and Psychology, Fairfield, USA

Reem Chaudhry Ehsanullah

Safeera khan.

Social media are responsible for aggravating mental health problems. This systematic study summarizes the effects of social network usage on mental health. Fifty papers were shortlisted from google scholar databases, and after the application of various inclusion and exclusion criteria, 16 papers were chosen and all papers were evaluated for quality. Eight papers were cross-sectional studies, three were longitudinal studies, two were qualitative studies, and others were systematic reviews. Findings were classified into two outcomes of mental health: anxiety and depression. Social media activity such as time spent to have a positive effect on the mental health domain. However, due to the cross-sectional design and methodological limitations of sampling, there are considerable differences. The structure of social media influences on mental health needs to be further analyzed through qualitative research and vertical cohort studies.

Introduction and background

Human beings are social creatures that require the companionship of others to make progress in life. Thus, being socially connected with other people can relieve stress, anxiety, and sadness, but lack of social connection can pose serious risks to mental health [ 1 ].

Social media

Social media has recently become part of people's daily activities; many of them spend hours each day on Messenger, Instagram, Facebook, and other popular social media. Thus, many researchers and scholars study the impact of social media and applications on various aspects of people’s lives [ 2 ]. Moreover, the number of social media users worldwide in 2019 is 3.484 billion, up 9% year-on-year [ 3 - 5 ]. A statistic in Figure  1  shows the gender distribution of social media audiences worldwide as of January 2020, sorted by platform. It was found that only 38% of Twitter users were male but 61% were using Snapchat. In contrast, females were more likely to use LinkedIn and Facebook. There is no denying that social media has now become an important part of many people's lives. Social media has many positive and enjoyable benefits, but it can also lead to mental health problems. Previous research found that age did not have an effect but gender did; females were much more likely to experience mental health than males [ 6 , 7 ].

Impact on mental health

Mental health is defined as a state of well-being in which people understand their abilities, solve everyday life problems, work well, and make a significant contribution to the lives of their communities [ 8 ]. There is debated presently going on regarding the benefits and negative impacts of social media on mental health [ 9 , 10 ]. Social networking is a crucial element in protecting our mental health. Both the quantity and quality of social relationships affect mental health, health behavior, physical health, and mortality risk [ 9 ]. The Displaced Behavior Theory may help explain why social media shows a connection with mental health. According to the theory, people who spend more time in sedentary behaviors such as social media use have less time for face-to-face social interaction, both of which have been proven to be protective against mental disorders [ 11 , 12 ]. On the other hand, social theories found how social media use affects mental health by influencing how people view, maintain, and interact with their social network [ 13 ]. A number of studies have been conducted on the impacts of social media, and it has been indicated that the prolonged use of social media platforms such as Facebook may be related to negative signs and symptoms of depression, anxiety, and stress [ 10 - 15 ]. Furthermore, social media can create a lot of pressure to create the stereotype that others want to see and also being as popular as others.

The need for a systematic review

Systematic studies can quantitatively and qualitatively identify, aggregate, and evaluate all accessible data to generate a warm and accurate response to the research questions involved [ 4 ]. In addition, many existing systematic studies related to mental health studies have been conducted worldwide. However, only a limited number of studies are integrated with social media and conducted in the context of social science because the available literature heavily focused on medical science [ 6 ]. Because social media is a relatively new phenomenon, the potential links between their use and mental health have not been widely investigated.

This paper attempt to systematically review all the relevant literature with the aim of filling the gap by examining social media impact on mental health, which is sedentary behavior, which, if in excess, raises the risk of health problems [ 7 , 9 , 12 ]. This study is important because it provides information on the extent of the focus of peer review literature, which can assist the researchers in delivering a prospect with the aim of understanding the future attention related to climate change strategies that require scholarly attention. This study is very useful because it provides information on the extent to which peer review literature can assist researchers in presenting prospects with a view to understanding future concerns related to mental health strategies that require scientific attention. The development of the current systematic review is based on the main research question: how does social media affect mental health?

Research strategy

The research was conducted to identify studies analyzing the role of social media on mental health. Google Scholar was used as our main database to find the relevant articles. Keywords that were used for the search were: (1) “social media”, (2) “mental health”, (3) “social media” AND “mental health”, (4) “social networking” AND “mental health”, and (5) “social networking” OR “social media” AND “mental health” (Table  1 ).

Out of the results in Table  1 , a total of 50 articles relevant to the research question were selected. After applying the inclusion and exclusion criteria, duplicate papers were removed, and, finally, a total of 28 articles were selected for review (Figure  2 ).

PRISMA, Preferred Reporting Items for Systematic Reviews and Meta-Analyses

Inclusion and exclusion criteria

Peer-reviewed, full-text research papers from the past five years were included in the review. All selected articles were in English language and any non-peer-reviewed and duplicate papers were excluded from finally selected articles.

Of the 16 selected research papers, there were a research focus on adults, gender, and preadolescents [ 10 - 19 ]. In the design, there were qualitative and quantitative studies [ 15 , 16 ]. There were three systematic reviews and one thematic analysis that explored the better or worse of using social media among adolescents [ 20 - 23 ]. In addition, eight were cross-sectional studies and only three were longitudinal studies [ 24 - 29 ].The meta-analyses included studies published beyond the last five years in this population. Table  2  presents a selection of studies from the review.

IGU, internet gaming disorder; PSMU, problematic social media use

This study has attempted to systematically analyze the existing literature on the effect of social media use on mental health. Although the results of the study were not completely consistent, this review found a general association between social media use and mental health issues. Although there is positive evidence for a link between social media and mental health, the opposite has been reported.

For example, a previous study found no relationship between the amount of time spent on social media and depression or between social media-related activities, such as the number of online friends and the number of “selfies”, and depression [ 29 ]. Similarly, Neira and Barber found that while higher investment in social media (e.g. active social media use) predicted adolescents’ depressive symptoms, no relationship was found between the frequency of social media use and depressed mood [ 28 ].

In the 16 studies, anxiety and depression were the most commonly measured outcome. The prominent risk factors for anxiety and depression emerging from this study comprised time spent, activity, and addiction to social media. In today's world, anxiety is one of the basic mental health problems. People liked and commented on their uploaded photos and videos. In today's age, everyone is immune to the social media context. Some teens experience anxiety from social media related to fear of loss, which causes teens to try to respond and check all their friends' messages and messages on a regular basis.

On the contrary, depression is one of the unintended significances of unnecessary use of social media. In detail, depression is limited not only to Facebooks but also to other social networking sites, which causes psychological problems. A new study found that individuals who are involved in social media, games, texts, mobile phones, etc. are more likely to experience depression.

The previous study found a 70% increase in self-reported depressive symptoms among the group using social media. The other social media influence that causes depression is sexual fun [ 12 ]. The intimacy fun happens when social media promotes putting on a facade that highlights the fun and excitement but does not tell us much about where we are struggling in our daily lives at a deeper level [ 28 ]. Another study revealed that depression and time spent on Facebook by adolescents are positively correlated [ 22 ]. More importantly, symptoms of major depression have been found among the individuals who spent most of their time in online activities and performing image management on social networking sites [ 14 ].

Another study assessed gender differences in associations between social media use and mental health. Females were found to be more addicted to social media as compared with males [ 26 ]. Passive activity in social media use such as reading posts is more strongly associated with depression than doing active use like making posts [ 23 ]. Other important findings of this review suggest that other factors such as interpersonal trust and family functioning may have a greater influence on the symptoms of depression than the frequency of social media use [ 28 , 29 ].

Limitation and suggestion

The limitations and suggestions were identified by the evidence involved in the study and review process. Previously, 7 of the 16 studies were cross-sectional and slightly failed to determine the causal relationship between the variables of interest. Given the evidence from cross-sectional studies, it is not possible to conclude that the use of social networks causes mental health problems. Only three longitudinal studies examined the causal relationship between social media and mental health, which is hard to examine if the mental health problem appeared more pronounced in those who use social media more compared with those who use it less or do not use at all [ 19 , 20 , 24 ]. Next, despite the fact that the proposed relationship between social media and mental health is complex, a few studies investigated mediating factors that may contribute or exacerbate this relationship. Further investigations are required to clarify the underlying factors that help examine why social media has a negative impact on some peoples’ mental health, whereas it has no or positive effect on others’ mental health.

Conclusions

Social media is a new study that is rapidly growing and gaining popularity. Thus, there are many unexplored and unexpected constructive answers associated with it. Lately, studies have found that using social media platforms can have a detrimental effect on the psychological health of its users. However, the extent to which the use of social media impacts the public is yet to be determined. This systematic review has found that social media envy can affect the level of anxiety and depression in individuals. In addition, other potential causes of anxiety and depression have been identified, which require further exploration.

The importance of such findings is to facilitate further research on social media and mental health. In addition, the information obtained from this study can be helpful not only to medical professionals but also to social science research. The findings of this study suggest that potential causal factors from social media can be considered when cooperating with patients who have been diagnosed with anxiety or depression. Also, if the results from this study were used to explore more relationships with another construct, this could potentially enhance the findings to reduce anxiety and depression rates and prevent suicide rates from occurring.

The content published in Cureus is the result of clinical experience and/or research by independent individuals or organizations. Cureus is not responsible for the scientific accuracy or reliability of data or conclusions published herein. All content published within Cureus is intended only for educational, research and reference purposes. Additionally, articles published within Cureus should not be deemed a suitable substitute for the advice of a qualified health care professional. Do not disregard or avoid professional medical advice due to content published within Cureus.

The authors have declared that no competing interests exist.

EDITORIAL article

Editorial: highlights in psychology: social anxiety.

• 1 Baltic Film, Media and Arts School, Tallinn University, Tallinn, Estonia
• 2 Department of Medicine and Surgery, University of Milano Bicocca, Monza, Italy

Editorial on the Research Topic Highlights in psychology: social anxiety

The aim of the Research Topic is to provide a comprehensive overview of the current research landscape surrounding social anxiety. Social anxiety is a pervasive mental health condition characterized by intense fear and discomfort in social situations, often leading to significant impairment in various areas of life such as relationships, work, and school.

Through this edition, the goal is to shed light on various aspects of social anxiety, including its cognitive, emotional, interpersonal, and cultural dimensions. The Research Topic seeks to showcase a diverse range of research methodologies and perspectives within the field of psychology, encompassing disciplines such as Personality and Social Psychology, Clinical Psychology, and Cognition.

The Research Topic delves into various specific themes, spanning errors in cognition like hypermentalizing and their correlation with social anxiety, along with exploring the repercussions of social anxiety on diverse relationship dynamics encompassing familial, romantic, professional, and platonic spheres. Additionally, it scrutinizes the comorbidity nexus between social anxiety and other mental health afflictions like depression and eating disorders, whilst also examining social anxiety across age demographics, from children to adolescents and young adults. The discourse extends to encompass assessment and treatment methodologies tailored for social anxiety, considering cultural dimensions including prevalence, manifestation, and treatment paradigms across different societies. Moreover, it investigates gender disparities and cultural influences on social anxiety, underlining the role of sociocultural factors in its formulation. Furthermore, it elucidates the intricate interplay between emotions, notably shame, and social anxiety, alongside delving into the neurobiological and psychophysiological underpinnings of this phenomenon.

The Research Topic contributes to our understanding of social anxiety and provide insights that can inform both theory and practice in psychology. This Research Topic includes articles that focus on social anxiety, demonstrating the wide range of research conducted in the field of Psychology, including areas such as Personality and Social Psychology, Clinical Psychology, and Cognition. Key conclusions drawn from the articles include the interdisciplinary nature of studying social anxiety, the introduction of concepts like “Alexinomia”, and the exploration of its relationships with other psychological factors such as olfactory reference disorder and childhood maltreatment. The role of personality traits, cultural influences, and technological advancements like social media are also highlighted, alongside the impact of current events such as the COVID-19 pandemic on social anxiety.

Articles within this Research Topic use methodologies from Personality and Social Psychology, Clinical Psychology, Cognition, and other related fields, highlighting the interdisciplinary nature of studying social anxiety. Several articles delve into the relationship between social anxiety and other disorders or conditions, such as olfactory reference disorder, childhood maltreatment, substance use disorders, and cognitive processing differences. This highlights the importance of understanding how social anxiety interacts with and may be influenced by other psychological factors.

The interplay between personality traits and social anxiety is a recurring theme, emphasizing the significance of individual differences in shaping the experience and expression of social anxiety. Cultural influences, such as self-construals among Chinese individuals, and technological advancements, such as social media use, are shown to have implications for social anxiety. These findings underscore the importance of considering cultural and technological contexts in understanding and addressing social anxiety.

Current events, such as the COVID-19 pandemic, can have significant implications for social anxiety and related behaviors. Understanding how contextual factors influence social anxiety is crucial for developing effective interventions and treatments. The exploration of therapeutic approaches, such as dialectical behavior therapy skills groups, suggests promising avenues for intervention in treating social anxiety disorder. Identifying effective treatment modalities is essential for improving outcomes for individuals with social anxiety.

This collection of articles enhances our comprehension of social anxiety across various domains, from its underlying mechanisms to its impact on individuals' lives, and explores potential avenues for intervention and treatment. Articles explore various aspects of social anxiety, including its interaction with different disorders, cognitive processes, technological influences, and cultural contexts. They also propose therapeutic approaches such as dialectical behavior therapy skills groups, aiming to improve interventions and treatments for social anxiety disorder. Each article contributes uniquely to the growing body of knowledge, shedding light on different aspects such as cognitive processing, cultural influences, therapeutic interventions, and the interplay with other psychological factors.

Ditye et al. introduce the concept of a specific fear related to social interaction.

Reuter et al. explore the relationship between specific disorders or conditions and social anxiety.

Okano and Nomura move into examining specific aspects of social anxiety and its interaction with other psychological factors.

Macovei et al. continue exploring the interplay between personality traits and social anxiety.

Liu et al. expand the discussion to include the influence of childhood experiences, cultural factors, and substance use disorders on social anxiety.

Zhu et al. shift focus to how social anxiety affects cognitive processes, particularly in interpreting non-verbal cues.

Yang et al. examine the relationship between social media use and social anxiety, adding a technological and cultural dimension to the discussion.

Thériault et al. explore the impact of social expectations and feedback on individuals with social anxiety.

Bagheri et al. offer a data-driven approach to understanding social dysfunction and its predictors, adding empirical evidence to the discussion.

Xia et al. consider the impact of current events (such as the COVID-19 pandemic) on social anxiety and related behaviors, incorporating relevant contextual factors.

Villalongo Andino et al. conclude by exploring potential therapeutic approaches for addressing social anxiety, suggesting avenues for intervention and treatment.

To conclude, the Research Topic deepens our understanding of social anxiety across multiple domains, offering insights into its mechanisms, impact on individuals' lives, and potential avenues for intervention and treatment.

Author contributions

AZ: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing—original draft, Writing—review & editing. AD: Writing—review & editing.

The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.

Acknowledgments

We are grateful to all authors who contributed to the research on the topic of Social Anxiety, all reviewers who added their efforts to improve the studies, and to Frontiers for their support.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Keywords: social anxiety (SA), cognition, relationships, mental health, depression, cultural differences, emotions, neurobiology

Citation: Zabrodskaja A and Dakanalis A (2024) Editorial: Highlights in psychology: social anxiety. Front. Psychol. 15:1404923. doi: 10.3389/fpsyg.2024.1404923

Received: 21 March 2024; Accepted: 15 April 2024; Published: 26 April 2024.

Edited and reviewed by: Gerald Matthews , George Mason University, United States

Copyright © 2024 Zabrodskaja and Dakanalis. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Anastassia Zabrodskaja, anastassia.zabrodskaja@gmail.com

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.