Health Misinformation Vs. Facts on Social Media: Co-Occurrence Network Analysis in Bangladesh
Bibliographic record
Abstract
The increased usage of social media provides a way to disseminate health-related information more quickly. Alternatively, sharing health content on social media poses risks due to unrestricted posting, enabling misinformation to spread. Regional social and cultural contexts influence themes in social media posts, underscoring the importance of understanding content and prevalent misinformation themes. This insight is crucial for tailoring interventions, resource allocation, misinformation detection algorithms, and policy formulation. We conducted word co-occurrence network analysis, creating and analyzing two networks for valid information and misinformation in Bangladesh. The prevalence of misinformation regarding natural ingredients and treatments in Bangladesh underscores the need for targeted efforts to combat health misinformation on social media. For each network, we computed metrics such as betweenness, Katz centrality, out-degree, and degree distribution. Furthermore, we computed the Louvain clustering algorithm to identify word clusters. A comparative analysis of both networks suggested that the context of words used in sentences was important and that both networks contained information about natural remedies or ingredients for health benefits. The misinformation network contained the word raw turmeric with the highest bigram frequency of 162. These natural remedies were stated as cures, and there was much misinformation and valid information surrounding common health conditions such as blood pressure. This was depicted through the word blood having an outdegree of four and seven in the misinformation and valid information networks, respectively. The valid information network emphasized the beneficial properties of natural ingredients rather than their supposed ability to cure diseases. This study provides insights into the distinctions and parallels between valid health information and misinformation on social media, considering their social and cultural context. It underscores shared semantics and bigram words between them, suggesting that understanding these differences can aid in addressing region-specific challenges.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".