Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2019 Jan;43(1):108-114.
doi: 10.1111/acer.13906. Epub 2018 Nov 19.

Discussions of Alcohol Use in an Online Social Network for Smoking Cessation: Analysis of Topics, Sentiment, and Social Network Centrality

Amy M Cohn  1   2 , Michael S Amato  3 , Kang Zhao  4 , Xi Wang  5 , Sarah Cha  3 , Jennifer L Pearson  6   7 , George D Papandonatos  8 , Amanda L Graham  2   3
Affiliations

Discussions of Alcohol Use in an Online Social Network for Smoking Cessation: Analysis of Topics, Sentiment, and Social Network Centrality

Amy M Cohn et al. Alcohol Clin Exp Res. 2019 Jan.

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.

Keywords: Alcohol; Online Cessation; Quitting; Relapse; Smoking; Social Networks; Text Mining; Topic Modeling.

PubMed Disclaimer

Conflict of interest statement

CONLFICTS OF INTEREST

There are no conflicts of interest to declare.

Comment in

References

    1. Adhikari B, Kahende J, Malarcher A, Pechacek T & Tong V 2009. Smoking-attributable mortality, years of potential life lost, and productivity losses. Oncology Times, 31, 40–42.
    1. Amato MS, Papandonatos GD, Cha S, Wang X, Zhao K, Cohn AM, Pearson JL & Graham AL 2018. Inferring Smoking Status from User Generated Content in an Online Cessation Community. Nicotine & Tobacco Research - PMC - PubMed
    1. Aral S & Nicolaides C 2017. Exercise contagion in a global social network. Nature communications, 8, 14753. - PMC - PubMed
    1. Bjartveit K & Tverdal A 2009. Health consequences of sustained smoking cessation. Tobacco control, tc. 2008026898. - PubMed
    1. Blei DM & Lafferty JD 2007. A correlated topic model of science. The Annals of Applied Statistics, 17–35.

Publication types