Social Media Mining to Support Long Covid Drug Repurposing: Exploring Self-medication Reported by Reddit Users (Preprint)

Abstract

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.