‘It’s just one big vicious circle’: young people’s experiences of highly visual social media and their mental health
Bibliographic record
Abstract
Highly visual social media (HVSM) platforms, such as Snapchat, Instagram and TikTok, are increasingly popular among young people. It is unclear what motivates young people to engage with these specific highly visual platforms and what impact the inherent features of HVSM have on young people's mental health. Nine semi-structured focus group sessions were conducted with males and females aged 14 and 15 years (n = 47) across five secondary schools in Northern Ireland. Thematic analyses were conducted, and a conceptual model was developed to illustrate the findings. This study found that features such as likes/comments on visuals and scrolling through a feed were associated with the role of '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 '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 instant inadequacy and instant gratification. HVSM appears to trigger an unrelenting process of emotional highs and lows for its adolescent users.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".