Accepted for/Published in: JMIR Formative Research
Date Submitted: May 16, 2022
Date Accepted: Aug 9, 2022
Date Submitted to PubMed: Aug 25, 2022
Social media mining to support drug repurposing: Exploring long-COVID self-medication reported by Reddit users
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.
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