Concerns among People who use Opioids during the COVID-19 Pandemic: A Natural Language Processing Analysis of Social Media Posts

Abstract

Background: Timely data from official sources regarding the impact of the COVID-19 pandemic on people who use prescription and illicit opioids (PWUOs) is lacking. We conducted a large-scale, natural language processing (NLP) analysis of conversations on opioid-related drug forums to better understand concerns among PWUOs. Methods: In this retrospective observational study, we analyzed posts from 14 opioid-related forums on the social network Reddit. We applied NLP to identify frequently mentioned substances and phrases, and grouped the phrases manually based on their contents into three broad key themes: (i) prescription and/or illicit opioid use; (ii) substance use disorder treatment access and care; and (iii) withdrawal. Phrases that were unmappable to any particular theme were discarded. We computed the frequencies of substance and theme mentions, and quantified their volumes over time. We compared changes in post volumes by key themes and substances between pre-COVID-19 (1/1/2019—2/29/2020) and COVID-19 (3/1/2020—11/30/2020) periods. Results: 77,652 and 119,168 posts were collected for the pre-COVID-19 and COVID-19 periods, respectively. By theme, posts about treatment and access to care increased by 300%, from 0.631 to 2.526 per 1000 posts between the pre-COVID-19 and COVID-19 periods. Conversations about withdrawal increased by 812% between the same periods (0.026 to 0.235 per 1,000 posts). Posts about drug use did not increase (0.219 to 0.218 per 1,000 posts). By substance, among medications for opioid use disorder, methadone had the largest increase in conversations (20.8 to 56.3 per 1,000 posts; 171.4% increase). Among other medications, posts about diphenhydramine exhibited the largest increase (0.3 to 0.9 per 1,000 posts; 171.8% increase). Conclusions: Conversations on opioid-related forums among PWUOs revealed increased concerns about treatment and access to care along with withdrawal following the emergence of COVID-19. Greater attention to social media data may help inform timely responses to the needs of PWUOs during COVID-19.

Experiments with LDA and Top2Vec for embedded topic discovery on social media data—A case study of cystic fibrosis

“…Among those applications, natural language processing (NLP) as a main computational approach has been employed to analyze social media in free text. Sarker et al (2022) analyzed posts from 14 opioid-related forums on the social network, Reddit using NLP, and compared concerns to treatment and access to care among people who use opioids before and during the COVID-19 pandemic. Furthermore, topic modeling, as an important NLP method, has been widely applied to identify hidden topics from social media to support biomedical research.…”