An Analysis of Demand Characteristics: Uncovering the True Effects of Social Media on Mental Health
Bibliographic record
Abstract
As social media use continues to rise, concerns about its effects on mental health remain debated. While some research links social media to adverse mental health outcomes, others highlight its benefits, such as social connectedness. Inconsistencies in research raise concerns that pre-existing beliefs may influence self-reported mental health outcomes in experimental settings, particularly when the negatives of social media are highlighted. This study examines whether individuals who strongly believe that social media is harmful will report poorer well-being, particularly when social media is framed in a negative light. A sample of 556 participants were initially screened through an online survey. Of these, 19 participants were selected and assigned to conditions emphasizing either the harms or benefits of social media, after which they completed mental health questionnaires in a laboratory setting. We hypothesized that negatively framed social media information would result in lower mental health scores, especially amongst those with negative pre-existing beliefs about social media. We aim to clarify whether the observed effects stem from actual social media use or biases introduced by experimental framing and participant expectations. We did notfind significant differences in mental health scores based on the social media framing conditions to which participants were assigned.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".