{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T01:55:20Z","timestamp":1778550920816,"version":"3.51.4"},"reference-count":12,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2020,3,22]],"date-time":"2020-03-22T00:00:00Z","timestamp":1584835200000},"content-version":"vor","delay-in-days":21,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/100000026","name":"National Institute on Drug Abuse","doi-asserted-by":"publisher","award":["K23DA042140\u201001"],"award-info":[{"award-number":["K23DA042140\u201001"]}],"id":[{"id":"10.13039\/100000026","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Drug and Alcohol Review"],"published-print":{"date-parts":[[2020,3]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Introduction and Aims<\/jats:title><jats:p>This article examines the feasibility of leveraging Twitter to detect posts authored by people who use opioids (PWUO) or content related to opioid use disorder (OUD), and manually develop a multidimensional taxonomy of relevant tweets.<\/jats:p><\/jats:sec><jats:sec><jats:title>Design and Methods<\/jats:title><jats:p>Twitter messages were collected between June and October 2017 (<jats:italic>n<\/jats:italic> =\u200923\u2009827) and evaluated using an inductive coding approach. Content was then manually classified into two axes (<jats:italic>n<\/jats:italic> =\u200917\u2009420): (i) user experience regarding accessing, using, or recovery from illicit opioids; and (ii) content categories (e.g. policies, medical information, jokes\/sarcasm).<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The most prevalent categories consisted of jokes or sarcastic comments pertaining to OUD, PWUOs or hypothetically using illicit opioids (63%), informational content about treatments for OUD, overdose prevention or accessing self\u2010help groups (20%), and commentary about government opioid policy or news related to opioids (17%). Posts by PWUOs centered on identifying illicit sources for procuring opioids (i.e. online, drug dealers; 49%), symptoms and\/or strategies to quell opioid withdrawal symptoms (21%), and combining illicit opioid use with other substances, such as cocaine or benzodiazepines (17%). State and public health experts infrequently posted content pertaining to OUD (1%).<\/jats:p><\/jats:sec><jats:sec><jats:title>Discussion and Conclusions<\/jats:title><jats:p>Twitter offers a feasible approach to identify PWUO. Further research is needed to evaluate the efficacy of Twitter to disseminate evidence\u2010based content and facilitate linkage to treatment and harm reduction services.<\/jats:p><\/jats:sec>","DOI":"10.1111\/dar.13048","type":"journal-article","created":{"date-parts":[[2020,3,23]],"date-time":"2020-03-23T03:13:02Z","timestamp":1584933182000},"page":"205-208","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Detecting illicit opioid content on Twitter"],"prefix":"10.1111","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7393-7564","authenticated-orcid":false,"given":"Babak","family":"Tofighi","sequence":"first","affiliation":[{"name":"Department of Population Health New York University School of Medicine  New York USA"},{"name":"Division of General Internal Medicine New York University School of Medicine  New York USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yindalon","family":"Aphinyanaphongs","sequence":"additional","affiliation":[{"name":"Department of Population Health New York University School of Medicine  New York USA"},{"name":"Department of Population Health, Center for Health Informatics and Bioinformatics  New York USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christina","family":"Marini","sequence":"additional","affiliation":[{"name":"Department of Population Health New York University School of Medicine  New York USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shouron","family":"Ghassemlou","sequence":"additional","affiliation":[{"name":"Medical College of Georgia  Augusta USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peyman","family":"Nayebvali","sequence":"additional","affiliation":[{"name":"Department of Population Health New York University School of Medicine  New York USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Isabel","family":"Metzger","sequence":"additional","affiliation":[{"name":"Department of Population Health, Center for Health Informatics and Bioinformatics  New York USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ananditha","family":"Raghunath","sequence":"additional","affiliation":[{"name":"Department of Population Health, Center for Health Informatics and Bioinformatics  New York USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shailin","family":"Thomas","sequence":"additional","affiliation":[{"name":"Department of Population Health New York University School of Medicine  New York USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2020,3,22]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.2196\/jmir.2503"},{"key":"e_1_2_7_3_1","doi-asserted-by":"publisher","DOI":"10.2196\/jmir.2534"},{"key":"e_1_2_7_4_1","doi-asserted-by":"crossref","unstructured":"AphinyanaphongsY RayB StatnikovA KrebsP. 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