Artificial Intelligence and Social Media for the Detection of Eating Disorders
Bibliographic record
Abstract
Artificial intelligence (AI) has been increasingly recognized for its potential in mental health management, including detecting, preventing, and treating eating disorders (EDs). Linardon et al. investigated current practices and perspectives on AI in ED treatment from professionals and community participants. While their work provides valuable insights into AI's role in ED management in the treatment phase, the applications of AI at earlier stages, particularly for case detection, and perspectives of key groups involved in this early-stage implementation (e.g., health professionals and individuals with or at risk of EDs) remain underexplored. Given the large volume of multimodal data available on social media platforms, together with their widespread use and accessibility, the integration of AI and social media provides an ideal opportunity for conducting large-scale, population-based detection for EDs. Thus, in this commentary, we discuss AI's potential to leverage social media data for case detection, highlight related ethical considerations (e.g., bias and data privacy), and propose future research directions.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".