ArticleJMIR formative research2022
Social Media Mining of Long-COVID Self-Medication Reported by Reddit Users: Feasibility Study to Support Drug Repurposing.
Article in JMIR formative research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed, 12 citations in OpenAlex.
- Self-management of long COVID symptoms with over-the-counter medicines and other non-prescribed therapies: a cross-sectional survey.BMC public health · 2026Article
- Thematic Mapping and Evolution of Social Media Mining in Health Research: Hybrid Bibliometric Synthesis.Journal of medical Internet research · 2026Review
- Social Listening in Infodemic Management: Insights from Preparatory Work on Ethical Guidance.Asian bioethics review · 2025Article
- Article
- The Role of Social Media in the Experiences of COVID-19 Among Long-Hauler Women: Qualitative Study.JMIR human factors · 2024Article
- Off-label drug use during the COVID-19 pandemic in Africa: topic modelling and sentiment analysis of ivermectin in South Africa and Nigeria as a case study.Journal of the Royal Society, Interface · 2023Article
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2 authors at 1 institution in 1 country.
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Abstract
backgroundSince the beginning of the COVID-19 pandemic, over 480 million people have been infected and more than 6 million people have died from COVID-19 worldwide. In some patients with acute COVID-19, symptoms manifest over a longer period, which is also called "long-COVID." Unmet medical needs related to long-COVID are high, since there are no treatments approved. Patients experiment with various medications and supplements hoping to alleviate their suffering. They often share their experiences on social media.
objectiveThe aim of this study was to explore the feasibility of social media mining methods to extract important compounds from the perspective of patients. The goal is to provide an overview of different medication strategies and important agents mentioned in Reddit users' self-reports to support hypothesis generation for drug repurposing, by incorporating patients' experiences.
methodsWe used named-entity recognition to extract substances representing medications or supplements used to treat long-COVID from almost 70,000 posts on the "/r/covidlonghaulers" subreddit. We analyzed substances by frequency, co-occurrences, and network analysis to identify important substances and substance clusters.
resultsThe named-entity recognition algorithm achieved an F1 score of 0.67. A total of 28,447 substance entities and 5789 word co-occurrence pairs were extracted. "Histamine antagonists," "famotidine," "magnesium," "vitamins," and "steroids" were the most frequently mentioned substances. Network analysis revealed three clusters of substances, indicating certain medication patterns.
conclusionsThis feasibility study indicates that network analysis can be used to characterize the medication strategies discussed in social media. Comparison with existing literature shows that this approach identifies substances that are promising candidates for drug repurposing, such as antihistamines, steroids, or antidepressants. In the context of a pandemic, the proposed method could be used to support drug repurposing hypothesis development by prioritizing substances that are important to users.
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