How objectively measured Twitter and Instagram use relate to self‐reported personality and tendencies toward Internet/Smartphone Use Disorder

Peterka‐Bonetta, Jessica; Sindermann, Cornelia; Elhai, Jon D.; Montag, Christian

doi:10.1002/hbe2.243

Cited by 12 publications

(8 citation statements)

References 66 publications

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“…Passive usage would reflect in browsing and simply consuming videos. The need to distinguish between active and passive use of social media has been also recently empirically supported by Peterka-Bonetta et al (49).…”

Section: Who Uses Tiktok and Who Does Not?mentioning

confidence: 88%

On the Psychology of TikTok Use: A First Glimpse From Empirical Findings

Montag

Yang

Elhai

2021

Front. Public Health

Self Cite

149

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TikTok (in Chinese: DouYin; formerly known as musical.ly) currently represents one of the most successful Chinese social media applications in the world. Since its founding in September 2016, TikTok has seen widespread distribution, in particular, attracting young users to engage in viewing, creating, and commenting on “LipSync-Videos” on the app. Despite its success in terms of user numbers, psychological studies aiming at an understanding of TikTok use are scarce. This narrative review provides a comprehensive overview on the small empirical literature available thus far. In particular, insights from uses and gratification theory in the realm of TikTok are highlighted, and we also discuss aspects of the TikTok platform design. Given the many unexplored research questions related to TikTok use, it is high time to strengthen research efforts to better understand TikTok use and whether certain aspects of its use result in detrimental behavioral effects. In light of user characteristics of the TikTok platform, this research is highly relevant because TikTok users are often adolescents and therefore from a group of potentially vulnerable individuals.

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Section: Who Uses Tiktok and Who Does Not?mentioning

confidence: 88%

On the Psychology of TikTok Use: A First Glimpse From Empirical Findings

Montag

Yang

Elhai

2021

Front. Public Health

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149

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“…Moreover, at the moment Facebook's APIs are closed for independent scientists, making it impossible for researchers to study actual behavior of its users . Therefore, it is not surprising that we only found a few studies investigating the association between user digital traces on social media and SNUD or excessive usage of the smartphone (i.e., Marino et al, 2017;Peterka-Bonetta et al, 2021). The study by Marino et al (2017) analyzed differences in objective digital footprints in problematic and non-problematic Facebook users.…”

Section: Introductionmentioning

confidence: 99%

Mining Digital Traces of Facebook Activity for the Prediction of Individual Differences in Tendencies Toward Social Networks Use Disorder: A Machine Learning Approach

et al. 2022

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More than three billion users are currently on one of Meta’s online platforms with Facebook being still their most prominent social media service. It is well known that Facebook has designed a highly immersive social media service with the aim to prolong online time of its users, as this results in more digital footprints to be studied and monetized (via psychological targeting). In this context, it is debated if social media platforms can elicit addictive behaviors. In the present work, we demonstrate in N = 1,094 users that it is possible to predict from digital footprints of the Facebook users their self-reported addictive tendencies toward social media (R > 0.30) by applying machine-learning strategies. More specifically, we analyzed the predictive power of a set of models based on different sets of features extracted from digital traces, namely posting activity, language use, and page Likes. To maximize the predictive power of the models, we used an ensemble of linear and non-linear prediction algorithms. This work showed also sufficient accuracy rates (AUC above 0.70) in distinguishing between disordered and non-disordered social media users. In sum, individual differences in tendencies toward “social networks use disorder” can be inferred from digital traces left on the social media platform Facebook. Please note that the present work is limited by its cross-sectional design.

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“…Very early research on the addictive use of the Internet was published in 1996 [8]. Many researchers prefer to use the term "Internet use disorder" [5,9] or "smartphone use disorder" [10][11][12], instead of "addiction"; however, these terms have still not been accepted by ICD-11 or DSM-5 [13].…”

Section: Introduction 1internet Smartphone or Tiktok Use Disordermentioning

confidence: 99%

Research on Adolescents Regarding the Indirect Effect of Depression, Anxiety, and Stress between TikTok Use Disorder and Memory Loss

Sha

Dong

2021

IJERPH

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This research involved the participation of 3036 Chinese students in the first and second years of senior high school. The adolescents were active users of TikTok. The mediating effect of depression, anxiety, and stress between TikTok use disorder and memory loss was investigated. A forward and backward digit span test was applied to measure memory loss. Structural equation modeling (SEM) was established, and SPSS Amos was used for analysis. The results show a partial mediation effect of depression and anxiety between TikTok use disorder and forward digit span. A partial mediation effect of depression, anxiety, and stress between TikTok use disorder and backward digit span is also shown. These results also show gender differences. Attention should be given to male students, who have more depression, anxiety, and stress than female students; they also have more memory loss.

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How objectively measured Twitter and Instagram use relate to self‐reported personality and tendencies toward Internet/Smartphone Use Disorder

Cited by 12 publications

References 66 publications

On the Psychology of TikTok Use: A First Glimpse From Empirical Findings

On the Psychology of TikTok Use: A First Glimpse From Empirical Findings

Mining Digital Traces of Facebook Activity for the Prediction of Individual Differences in Tendencies Toward Social Networks Use Disorder: A Machine Learning Approach

Research on Adolescents Regarding the Indirect Effect of Depression, Anxiety, and Stress between TikTok Use Disorder and Memory Loss

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