Information Sources and Needs in the Obesity and Diabetes Twitter Discourse
Bibliographic record
Abstract
Obesity and diabetes epidemics are affecting about a third and tenth of US population, respectively, capturing the attention of the nation and its institutions. Social media provides an open forum for communication between individuals and health organizations, a forum which is easily joined by parties seeking to gain profit from it. In this paper we examine 1.5 million tweets mentioning obesity and diabetes in order to assess (1) the quality of information circulating in this conversation, as well as (2) the behavior and information needs of the users engaged in it. The analysis of top cited domains shows a strong presence of health information sources which are not affiliated with a governmental or academic institution at 41% in obesity and 50% diabetes samples, and that tweets containing these domains are retweeted more than those containing domains of reputable sources. On the user side, we estimate over a quarter of non-informational obesity discourse to contain fat-shaming -- a practice of humiliating and criticizing overweight individuals -- with some self-directed toward the writers themselves. We also find a great diversity in questions asked in these datasets, spanning definition of obesity as a disease, social norms, and governmental policies. Our results indicate a need for addressing the quality control of health information on social media, as well as a need to engage in a topically diverse, psychologically charged discourse around these diseases.
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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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.000 | 0.004 |
| 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".