Background Since 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. Objective The 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. Methods We 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. Results The 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. Conclusions This 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.
BACKGROUND Since the beginning of the COVID-19 pandemic, over 480 million people have been infected, and more than 6 million people died from COVID-19 worldwide. In some patients with acute COVID-19, symptoms manifest over a longer period, also called “Long Covid”. Unmet medical need related to long covid is high, since there are no treatments approved. OBJECTIVE The study aims to provide an overview of different medication treatment strategies and important compounds, mentioned in reddit users long-covid self-reports, to support drug repurposing hypothesis generation by applying the principle of retrospective clinical analysis using passive crowdsourcing. METHODS We 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. Substances were analyzed by frequency, co-occurrences, and network analysis, to identify important substances and clusters of substances. RESULTS The named entity recognition algorithm achieved an F1 score of 0.67. A total of 28,447 substance entities and 5,789 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. CONCLUSIONS Our results highlight certain approaches to drug repurposing, such as antihistamines, steroids, or antidepressants, while also indicating that patients experiment with a wide range of substances in a systematic manner. In the context of a pandemic, passive crowdsourcing of potential treatments can support drug repurposing hypothesis development by prioritizing substances that are important to users.
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