If the Mask Fits: Psychological Correlates with Online Self-Presentation Experimentation in Adults

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Abstract

Online self-presentation refers to the ways in which individuals share aspects of the self to portray a particular image. Being online presents opportunities for individuals to experiment with different versions of the self as part of identity development but also to manage how others perceive them. Research has shown that personality can influence online self-presentation behaviors, but these studies have mainly focused on internal characteristics, and more research is needed exploring the relational facets of personality. This study aims to investigate the extent to which an individual's self-concept clarity, self-monitoring tendency, self-esteem, and social anxiety predict different presentations of the online self. A cross-sectional online survey was conducted with 405 adult participants from Australia, the United Kingdom, and the United States. Results show that individuals with higher self-concept clarity and self-monitoring are more likely to present a single consistent online and offline self. Younger adults and those with greater social anxiety are more likely to present idealized self-images online, and participants with higher social anxiety and lower self-esteem are more likely to prefer online, rather than offline, communication. Findings are broadly consistent with the literature, and suggest the need for more systematic investigation into a variety of personality variables that take into account the relational nature of identity formation and impression management. This research emphasizes the multifaceted nature of online self-presentation behaviors, and the ways in which they are differentially influenced by personality variables.

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Fullwood, C., Wesson, C., Chen-Wilson, J., Keep, M., Asbury, T., & Wilsdon, L. (2020). If the Mask Fits: Psychological Correlates with Online Self-Presentation Experimentation in Adults. Cyberpsychology, Behavior, and Social Networking, 23(11), 737–742. https://doi.org/10.1089/cyber.2020.0154

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