Instagram as a Window to Societal Perspective on Mental Health, Gender, and Race: Observational Pilot Study
Bibliographic record
Abstract
BACKGROUND: Gender and race are known to impact attitudes toward mental health topics and help-seeking behavior. Men and minorities are more likely to cite stigma as a reason for not seeking help for mental health concerns, which is of particular relevance given the high rate of suicide in men and challenges of historic proportion currently facing minority communities. Instagram provides a platform to discuss mental health, though a lack of male and minority representation may further alienate these populations. OBJECTIVE: We aimed to investigate whether men and nonwhite individuals are underrepresented in Instagram photos tagged with #mentalhealth (compared to photos tagged with #health) to better understand how gender and race-based representations are manifested on this popular social media platform and discuss the implications. METHODS: Three investigators of different genders and racial backgrounds met on nine different days via teleconference to analyze a total of 215 publicly available Instagram photos tagged with #mentalhealth and 215 with #health. These photos were generated using Instagram's search function, and search results were sorted by most recently published at the time of data collection. For each photo, the three investigators recorded their observations about the gender (male versus female) and race (white versus nonwhite versus racially unclassifiable) of subjects featured in the photo, which they did not discuss with other investigators. Chi-squared analysis was performed on each investigator's data set to compare the frequency of male versus female and white versus nonwhite subjects identified in each hashtag category. Kappa interrater agreement was calculated for each investigator pair, category (gender or race), and hashtag. RESULTS: values range from 11.912 to 98.927, P<.001 for all). Kappa interrater agreement revealed almost perfect agreement for gender (kappa=0.908-0.992) with the agreement for race ranging from 0.614 to 0.822, depending on hashtag and rater pair. CONCLUSIONS: Women are featured more frequently than men in Instagram photos tagged with #mentalhealth. The topic of #health, meanwhile, is not gendered this way. Low visibility of mental health among men may both represent and exacerbate existing stigma and barriers to care. White subjects are featured significantly more frequently than nonwhite subjects in photos tagged with both #mentalhealth and #health. Directed interventions using the Instagram platform may be indicated to increase the visibility of underrepresented groups and break the cycle of stigma.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".