Amy M Cohn1,2, Michael S Amato3, Kang Zhao4, Xi Wang5, Sarah Cha3, Jennifer L Pearson6,7, George D Papandonatos8, Amanda L Graham2,3. 1. Battelle Memorial Institute, Arlington, Virginia. 2. Department of Oncology, Georgetown University Medical Center, Washington, District of Columbia. 3. Schroeder Institute at Truth Initiative, Washington, District of Columbia. 4. Department of Management Sciences, The University of Iowa, Iowa City, Iowa. 5. School of Information, Central University of Finance and Economics, Beijing, China. 6. School of Community Health Sciences, University of Nevada, Reno, Nevada. 7. Department of Health, Behavior and Society, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland. 8. Center for Statistical Sciences, Brown University, Providence, Rhode Island.
Abstract
BACKGROUND: Few Internet smoking cessation programs specifically address the impact of alcohol use during a quit attempt, despite its common role in relapse. This study used topic modeling to describe the most prevalent topics about alcohol in an online smoking cessation community, the prevalence of negative sentiment expressed about alcohol use in the context of a quit attempt (i.e., alcohol should be limited or avoided during a quit attempt) within topics, and the degree to which topics differed by user social connectivity within the network. METHODS: Data were analyzed from posts from the online community of a larger Internet cessation program, spanning January 1, 2012 to May 31, 2015 and included records of 814,258 online posts. Posts containing alcohol-related content (n = 7,199) were coded via supervised machine learning text classification to determine whether the post expressed negative sentiment about drinking in the context of a quit attempt. Correlated topic modeling (CTM) was used to identify a set of 10 topics of at least 1% prevalence based on the frequency of word occurrences among alcohol-related posts; the distribution of negative sentiment and user social network connectivity was examined across the most salient topics. RESULTS: Three salient topics (with prevalence ≥10%) emerged from the CTM, with distinct themes of (i) cravings and temptations; (ii) parallel between nicotine addiction and alcoholism; and (iii) celebratory discussions of quit milestones including "virtual" alcohol use and toasts. Most topics skewed toward nonnegative sentiment about alcohol. The prevalence of each topic differed by users' social connectivity in the network. CONCLUSIONS: Future work should examine whether outcomes in Internet interventions are improved by tailoring social network content to match user characteristics, topics, and network behavior.
BACKGROUND: Few Internet smoking cessation programs specifically address the impact of alcohol use during a quit attempt, despite its common role in relapse. This study used topic modeling to describe the most prevalent topics about alcohol in an online smoking cessation community, the prevalence of negative sentiment expressed about alcohol use in the context of a quit attempt (i.e., alcohol should be limited or avoided during a quit attempt) within topics, and the degree to which topics differed by user social connectivity within the network. METHODS: Data were analyzed from posts from the online community of a larger Internet cessation program, spanning January 1, 2012 to May 31, 2015 and included records of 814,258 online posts. Posts containing alcohol-related content (n = 7,199) were coded via supervised machine learning text classification to determine whether the post expressed negative sentiment about drinking in the context of a quit attempt. Correlated topic modeling (CTM) was used to identify a set of 10 topics of at least 1% prevalence based on the frequency of word occurrences among alcohol-related posts; the distribution of negative sentiment and user social network connectivity was examined across the most salient topics. RESULTS: Three salient topics (with prevalence ≥10%) emerged from the CTM, with distinct themes of (i) cravings and temptations; (ii) parallel between nicotine addiction and alcoholism; and (iii) celebratory discussions of quit milestones including "virtual" alcohol use and toasts. Most topics skewed toward nonnegative sentiment about alcohol. The prevalence of each topic differed by users' social connectivity in the network. CONCLUSIONS: Future work should examine whether outcomes in Internet interventions are improved by tailoring social network content to match user characteristics, topics, and network behavior.
Authors: Christopher W Kahler; Ron Borland; Andrew Hyland; Sherry A McKee; Mary E Thompson; K Michael Cummings Journal: Drug Alcohol Depend Date: 2008-12-03 Impact factor: 4.492
Authors: Kang Zhao; Xi Wang; Sarah Cha; Amy M Cohn; George D Papandonatos; Michael S Amato; Jennifer L Pearson; Amanda L Graham Journal: J Med Internet Res Date: 2016-08-25 Impact factor: 5.428