Blame and epidemics: A methodological commentary on qualitative social media research
Bibliographic record
Abstract
Reactions of blame are recurring in the history of epidemics. Qualitative methods have proved to be an important tool to understand the social, cultural, political and emotional complexities of these accusatory dynamics. This methodological commentary examines tendencies and challenges when such qualitative studies are conducted on social media. It relies on a structured literature review (n=60) and on the authors’ own experiences from 15 years of qualitatively researching blame on social media during various outbreaks of diseases. Four main considerations emerge. First, the overreliance on X (formerly Twitter), explained by this platform’s data availability, offers limited materials to conduct in-depth qualitative analysis. Second, traditional qualitative approaches tend to be transposed to social media, in a manner that may neglect the specificities of this data. Third, the analyzed data is typically anonymized and decontextualized, due to ethical concerns and platform constraints. Finally, researchers often use methods bordering on quantitative processes. In light of these tendencies, we discuss broad considerations for future research.
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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 | 0.263 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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; both teacher heads agree on what is shown here.
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".