#MyDepressionLooksLike: Examining Public Discourse About Depression on Twitter
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Social media provides a context for billions of users to connect, express sentiments, and provide in-the-moment status updates. Because Twitter users tend to tweet emotional updates from daily life, the platform provides unique insights into experiences of mental health problems. Depression is not only one of the most prevalent health conditions but also carries a social stigma. Yet, opening up about one's depression and seeking social support may provide relief from symptoms. OBJECTIVE: The aim of this study was to examine the public discourse of the trending hashtag #MyDepressionLooksLike to look more closely at how users talk about their depressive symptoms on Twitter. METHODS: We captured 3225 original content tweets for the hashtag #MyDepressionLooksLike that circulated in May of 2016. Eliminating public service announcements, spam, and tweets with links to pictures or videos resulted in a total of 1978 tweets. Using qualitative content analysis, we coded the tweets to detect themes. RESULTS: The content analysis revealed seven themes: dysfunctional thoughts, lifestyle challenges, social struggles, hiding behind a mask, apathy and sadness, suicidal thoughts and behaviors, and seeking relief. CONCLUSIONS: The themes revealed important information about the content of the public messages that people share about depression on Twitter. More research is needed to understand the effects of the hashtag on increasing social support for users and reducing social stigma related to depression.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it