Health Misinformation Vs. Facts on Social Media: Co-Occurrence Network Analysis in Bangladesh
DOI:
https://doi.org/10.34190/ecsm.11.1.2336Keywords:
Health, Social media, Misinformation, Social Network analysis, Word Co-occurrence NetworkAbstract
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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