Searching for <i>Thinspiration</i> : The Nature of Internet Searches for Pro-Eating Disorder Websites
Bibliographic record
Abstract
Pro-eating disorder (Pro-ED) Websites are widespread and easily accessible. The risks associated with these Websites (e.g., reinforcing and encouraging eating disordered behaviors) have received interest from researchers, media, and mental health professionals. However, little is known about how these Websites are found, which may provide insight into prevention and intervention initiatives. Using Google AdWords Keywords in February 2011, a series of search terms (based on previous research) were entered to generate search-related data regarding actual pro-ED terms used in Google, including the corresponding search results, which were coded for degree of potential harm. Results indicated that Pro-ED search terms are sought out more than 13 million times annually, with pro ana receiving the most searches monthly. Overall, different terms are associated with varying numbers of monthly searches and regional interest. Search terms with references to thinspiration and thinspo are associated with the most harmful Website content. To provide those who seek pro-ED content with helpful, research-supported resources, it may be important to intervene before the point of access by targeting the search results corresponding with pro-ED search terms. Efforts may need to pay particular attention to terms such as thinspiration and thinspo.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".