A Scoping Review of User Characteristics Moderating the Relationship Between Use of Social Networking Sites and Mental Health

The relationship between social networking sites (SNS) and mental health is influenced by individual user characteristics, making it crucial to understand the nature of this complex interaction. Despite its importance, however, there is a lack of comprehensive reviews synthesizing prior studies on how user characteristics moderate the relationship between usage of SNS and mental health. Therefore, to examine how this issue has been studied in the literature, we conducted a scoping review, focusing on quantitative evidence, including experimental, cross-sectional, longitudinal, and experience-sampling studies. We provide an overview of (a) user characteristics that have been examined as moderators of the relationship between (overall, active, or passive) SNS use and mental health (well-being or ill-being), and (b) methodological and theoretical features of these studies. After conducting a systematic database search, followed by title and abstract screening, full-text review of relevant studies, and an interrater reliability check among three authors, we found 30 journal articles examining 24 unique moderators. We then applied a narrative synthesis approach to describe the findings. Most studies were atheoretical, cross-sectional, and relied on convenience samples. Most moderators (n = 16, 67%) were examined in a single journal article only and for those examined at least twice (gender, social comparison orientation, neuroticism, extraversion, SNS use motives, Fear of missing out, self-esteem, social capital & social support, and age), results were mixed. However, experimental studies revealed that social comparison orientation was the most consistent moderator, aggravating the negative consequences of passive SNS use on mental health. We outline four recommendations for future studies on the conditional effects of SNS use on mental health to resolve existing inconsistencies and deepen our understanding of these conditional effects: (a) improving participant sampling, (b) refining the conceptualization and measurement of key variables, (c) accounting for platform diversity, and (d) incorporating theoretical frameworks.