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Social Media Mining of Long-COVID Self-Medication Reported by Reddit Users: Feasibility Study to Support Drug Repurposing

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...

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Detalles Bibliográficos
Autores principales: Koss, Jonathan, Bohnet-Joschko, Sabine
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9531770/
https://www.ncbi.nlm.nih.gov/pubmed/36007131
http://dx.doi.org/10.2196/39582
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author Koss, Jonathan
Bohnet-Joschko, Sabine
author_facet Koss, Jonathan
Bohnet-Joschko, Sabine
author_sort Koss, Jonathan
collection PubMed
description 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.
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spelling pubmed-95317702022-10-05 Social Media Mining of Long-COVID Self-Medication Reported by Reddit Users: Feasibility Study to Support Drug Repurposing Koss, Jonathan Bohnet-Joschko, Sabine JMIR Form Res Original Paper 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. JMIR Publications 2022-10-03 /pmc/articles/PMC9531770/ /pubmed/36007131 http://dx.doi.org/10.2196/39582 Text en ©Jonathan Koss, Sabine Bohnet-Joschko. Originally published in JMIR Formative Research (https://formative.jmir.org), 03.10.2022. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Formative Research, is properly cited. The complete bibliographic information, a link to the original publication on https://formative.jmir.org, as well as this copyright and license information must be included.
spellingShingle Original Paper
Koss, Jonathan
Bohnet-Joschko, Sabine
Social Media Mining of Long-COVID Self-Medication Reported by Reddit Users: Feasibility Study to Support Drug Repurposing
title Social Media Mining of Long-COVID Self-Medication Reported by Reddit Users: Feasibility Study to Support Drug Repurposing
title_full Social Media Mining of Long-COVID Self-Medication Reported by Reddit Users: Feasibility Study to Support Drug Repurposing
title_fullStr Social Media Mining of Long-COVID Self-Medication Reported by Reddit Users: Feasibility Study to Support Drug Repurposing
title_full_unstemmed Social Media Mining of Long-COVID Self-Medication Reported by Reddit Users: Feasibility Study to Support Drug Repurposing
title_short Social Media Mining of Long-COVID Self-Medication Reported by Reddit Users: Feasibility Study to Support Drug Repurposing
title_sort social media mining of long-covid self-medication reported by reddit users: feasibility study to support drug repurposing
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9531770/
https://www.ncbi.nlm.nih.gov/pubmed/36007131
http://dx.doi.org/10.2196/39582
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