"It's just one big vicious circle"

: A mixed-methods study on the relationship between highly visual social media and young people's mental health in Northern Ireland

Abstract

Highly Visual Social Media (HVSM) platforms, such as Snapchat, Instagram and TikTok, are increasingly popular among young people. It is unclear what impact engagement with HVSM has on young people’s mental health in comparison to Less Visual Social Media (LVSM). It is also uncertain what motivates young people to use these platforms and the impact inherent features of HVSM have on their mental health. A sequential explanatory mixed-methods study was conducted, comprising of 438 questionnaires (Phase One) and nine semi-structured online focus groups (n=47, Phase Two). Participants were males and females aged 14-15 years-old from five secondary schools across Northern Ireland. Findings from Phase One showed that the greater intensity of time reported with HVSM users, the higher the levels of anxiety, depression, loneliness, and self-esteem. The more the use of photographic filters across HVSM and LVSM, the higher the levels of anxiety and self-esteem. HVSM users reported a more negative emotional reaction compared to LVSM participants. Phase two found that features such as likes/comments on visuals and scrolling through a feed were associated with the role of ‘passive viewer,’ instigating longer lasting feelings of jealousy, inferiority, and pressure to be accepted. To combat these negative emotions, young people turn to the role of ‘active contributor’ by using filters, selecting highlights to post to their feed and adjusting their personas, resulting in temporary feelings of higher self-esteem, greater acceptance, and popularity. As users of HVSM are constantly switching between the role of viewer and contributor, the emotions they experience are also constantly switching between inadequacy and gratification. HVSM appears to trigger an unrelenting process of emotional highs and lows for its adolescent users, as depicted in this study’s conceptual model.

Date of Award	Dec 2022
Original language	English
Awarding Institution	Queen's University Belfast
Supervisor	Paul Best (Supervisor) & Alan Maddock (Supervisor)