A Twitter discourse analysis of negative feelings and stigma related to NAFLD, NASH and obesity
Bibliographic record
Abstract
BACKGROUND: People with non-alcoholic fatty liver disease (NAFLD) and non-alcoholic steatohepatitis (NASH) are stigmatized, partly since 'non-alcoholic' is in the name, but also because of obesity, which is a common condition in this group. Stigma is pervasive in social media and can contribute to poorer health outcomes. We examine how stigma and negative feelings concerning NAFLD/NASH and obesity manifest on Twitter. METHODS: Using a self-developed search terms index, we collected NAFLD/NASH tweets from May to October 2019 (Phase I). Because stigmatizing NAFLD/NASH tweets were limited, Phase II focused on obesity (November-December 2019). Via sentiment analysis, >5000 tweets were annotated as positive, neutral or negative and used to train machine learning-based Natural Language Processing software, applied to 193 747 randomly sampled tweets. All tweets collected were analysed. RESULTS: In Phase I, 16 835 tweets for NAFLD and 2376 for NASH were retrieved. Of the annotated NAFLD/NASH tweets, 97/1130 (8.6%) and 63/535 (11.8%), respectively, related to obesity and 13/1130 (1.2%) and 5/535 (0.9%), to stigma; they primarily focused on scientific discourse and unverified information. Of the 193 747 non-annotated obesity tweets (Phase II), the algorithm classified 40.0% as related to obesity, of which 85.2% were negative, 1.0% positive and 13.7% neutral. CONCLUSIONS: NAFLD/NASH tweets mostly indicated an unmet information need and showed no clear signs of stigma. However, the negative content of obesity tweets was recurrent. As obesity-related stigma is associated with reduced care engagement and lifestyle modification, the main NAFLD/NASH treatment, stigma-reducing interventions in social media should be included in the liver health agenda.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".