{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T16:33:23Z","timestamp":1791477203841,"version":"4.3.4"},"reference-count":75,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Infodemiology"],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>The growing availability of big data spontaneously generated by social media platforms allows us to leverage natural language processing (NLP) methods as valuable tools to understand the opioid crisis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>We aimed to understand how NLP has been applied to Reddit (Reddit Inc) data to study opioid use.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We systematically searched for peer-reviewed studies and conference abstracts in PubMed, Scopus, PsycINFO, ACL Anthology, IEEE Xplore, and Association for Computing Machinery data repositories up to July 19, 2022. Inclusion criteria were studies investigating opioid use, using NLP techniques to analyze the textual corpora, and using Reddit as the social media data source. We were specifically interested in mapping studies\u2019 overarching goals and findings, methodologies and software used, and main limitations.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>In total, 30 studies were included, which were classified into 4 nonmutually exclusive overarching goal categories: methodological (n=6, 20% studies), infodemiology (n=22, 73% studies), infoveillance (n=7, 23% studies), and pharmacovigilance (n=3, 10% studies). NLP methods were used to identify content relevant to opioid use among vast quantities of textual data, to establish potential relationships between opioid use patterns or profiles and contextual factors or comorbidities, and to anticipate individuals\u2019 transitions between different opioid-related subreddits, likely revealing progression through opioid use stages. Most studies used an embedding technique (12\/30, 40%), prediction or classification approach (12\/30, 40%), topic modeling (9\/30, 30%), and sentiment analysis (6\/30, 20%). The most frequently used programming languages were Python (20\/30, 67%) and R (2\/30, 7%). Among the studies that reported limitations (20\/30, 67%), the most cited was the uncertainty regarding whether redditors participating in these forums were representative of people who use opioids (8\/20, 40%). The papers were very recent (28\/30, 93%), from 2019 to 2022, with authors from a range of disciplines.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This scoping review identified a wide variety of NLP techniques and applications used to support surveillance and social media interventions addressing the opioid crisis. Despite the clear potential of these methods to enable the identification of opioid-relevant content in Reddit and its analysis, there are limits to the degree of interpretive meaning that they can provide. Moreover, we identified the need for standardized ethical guidelines to govern the use of Reddit data to safeguard the anonymity and privacy of people using these forums.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.2196\/51156","type":"journal-article","created":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T11:31:19Z","timestamp":1726227079000},"page":"e51156","source":"Crossref","is-referenced-by-count":10,"title":["The Use of Natural Language Processing Methods in Reddit to Investigate Opioid Use: Scoping 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