A qualitative review of social media sharing and the 2022 monkeypox outbreak: did early labelling help to curb misinformation or fuel the fire?
Bibliographic record
Abstract
BACKGROUND: Misinformation, defined as a claim that is false or misleading, considers information that is both shared with the intention of causing harm, and information that is false with no ill intent. Early attempts to downplay the risk of monkeypox (mpox) by singling out men who have sex with men (MSM) may have had the ill effect of stigmatising this group in discussions online. The aim of this study was to evaluate themes present on Instagram related to the 2022 mpox outbreak under #monkeypox. Specifically, this study sought to determine if the pervasive narratives surrounding the coronavirus disease 2019 (COVID-19) pandemic, particularly related to government mistrust and conspiracy, were penetrating discussions about mpox. METHODS: A total of 255 posts under #monkeypox (the top 85 posts per day, every 10days in July 2022) were collected on Instagram. A content analysis approach, which seeks to quantify themes present, was utilised to evaluate themes present in posts under #monkeypox. RESULTS: Contrary to previous research investigating public health misinformation online, the majority of posts under #monkeypox were categorised as accurate information (85.9%). Moreover, a surprising number of posts were classified as anti-misinformation (32.9%), whereby users actively worked to debunk false information being shared online related to mpox. CONCLUSIONS: We hypothesise that early labelling of the disease as one that strictly affects online MSM communities has resulted in the digital community coming together to fact-check and debunk misinformation under #monkeypox on Instagram.
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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 | 0.035 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".