Abstract

     In adolescence, smartphone use in general and social media use in particular has often been associated with negative effects, such as higher anxiety levels and body dissatisfaction. Other outcomes – such as fundamental cognitive abilities and skills (e.g., intelligence, information processing, spatial perception) – have rarely been the focus of research. Here, we analysed data from a large sample of adolescents (12–۱۶ years; N > 12,000) who performed a series of psychometric tests ranging from intelligence, spatial perception, and information processing, to practical numeracy, and compared their test results with their social media usage (average active and passive time per day, problematic social media use). We additionally applied a random-forest model approach, useful for designs with many predictors and expected small effect sizes. Almost all associations did not outperform known age- and sex-differences on social media use; that is, effect sizes were small-to-tiny and had low importance in the random-forest analyses compared to dominant demographic effects. Negative effects of social media use may have been overstated in past research, at least in samples with adolescents.

Introduction

     In the early days of the Internet, communication was typically unidirectional and often time-staggered, that is, real-time communication was rarely possible. Since then, however, Internet-mediated communication has become so fast that it is possible to communicate with people worldwide with only very small time delays, so much so that we do not truly see much difference compared with face-to-face communication. This transformation from unidirectional, time-staggered communication to bidirectional, almost real-time communication has often been termed Web 2.0 or social media (Ellison & boyd, 2013). Since then, research has analysed what impact this transformation may have had on people, especially adolescents as frequent users, and how this could be explained.

     One example is the transformation framework (Nesi et al., 2018), which focuses on the impact of social media-based communication on adolescents’ peer experiences (i.e., experiences typically occurring between two or more individuals). It is assumed that this impact is based on seven key differences between face-to-face and online communication via social media. Specifically, social media is mostly asynchronous (i.e., there is time lapse due to the time taken to construct messages, though videoconferencing is an exception), permanent (i.e., texts and other content is stored or can be recorded), public (i.e., usually accessible by large audiences), almost universally available (i.e., can be shared regardless of physical location), lacks certain cues (i.e., physical cues such as gesture may be absent), quantifiable (i.e., use of social metrics, such as likes), and visual (i.e., use of photographs and videos).

Results

     Looking at Table 1 and Fig. 1, the results of the multiple linear regressions and RF models are clear. First, explained variance percentages were low (<7.1%), bearing in mind the number of predictors in the model (k = 18). Second, even the strongest predictors for each social media use indicator (e.g., PSMU, average social media use per day) revealed only low effect sizes, from β = −۰٫۰۷۱ to 0.116 (PSMU binary: sex; PSMU likert: practical numeracy; time active per day: sex; time passive per day: practical numeracy; Table 1). Third, depending on the indicator of social media use (PSMU vs. average social media use per day), we see inconsistent patterns of rather tiny effect sizes. This suggests that PSMU might be different from social media use (zero-order correlations between these concepts: r = 0.26–۰٫۲۸), which is perhaps surprising because usage duration should be one of the strongest indicators of problematic social media use. Fourth, if we compare the predictors for active and passive social media use, we do not see much difference except substantially lower explained variance for passive use (1.6% vs. 7.1% for active use respectively). In past research, passive use (in comparison to active use) was associated with negative aspects (e.g., stronger symptoms of anxiety and depressed mood: e.g., Escobar-Viera et al., 2018; Thorisdottir et al., 2019); therefore, we expected a more differentiated pattern of significant predictors between active and passive use, which is not apparent (see Table 1).