Literature DB >> 26640831

Characterizing Smoking and Drinking Abstinence from Social Media.

Acar Tamersoy, Munmun De Choudhury, Duen Horng Chau.   

Abstract

Social media has been established to bear signals relating to health and well-being states. In this paper, we investigate the potential of social media in characterizing and understanding abstinence from tobacco or alcohol use. While the link between behavior and addiction has been explored in psychology literature, the lack of longitudinal self-reported data on long-term abstinence has challenged addiction research. We leverage the activity spanning almost eight years on two prominent communities on Reddit: StopSmoking and StopDrinking. We use the self-reported "badge" information of nearly a thousand users as gold standard information on their abstinence status to characterize long-term abstinence. We build supervised learning based statistical models that use the linguistic features of the content shared by the users as well as the network structure of their social interactions. Our findings indicate that long-term abstinence from smoking or drinking (~one year) can be distinguished from short-term abstinence (~40 days) with 85% accuracy. We further show that language and interaction on social media offer powerful cues towards characterizing these addiction-related health outcomes. We discuss the implications of our findings in social media and health research, and in the role of social media as a platform for positive behavior change and therapy.

Entities:  

Keywords:  Reddit; abstinence; addiction; drinking; health; smoking; social media; well-being

Year:  2015        PMID: 26640831      PMCID: PMC4668115          DOI: 10.1145/2700171.2791247

Source DB:  PubMed          Journal:  HT ACM Conf Hypertext Soc Media


  21 in total

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Journal:  Cyberpsychol Behav Soc Netw       Date:  2013-04-25

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10.  The collective dynamics of smoking in a large social network.

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Journal:  N Engl J Med       Date:  2008-05-22       Impact factor: 91.245

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  16 in total

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Journal:  Health Commun       Date:  2017-08-09

6.  Harnessing Psycho-lingual and Crowd-Sourced Dictionaries for Predicting Taboos in Written Emotional Disclosure in Anonymous Confession Boards.

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Journal:  J Healthc Inform Res       Date:  2021-04-30

7.  Mining User-Generated Content in an Online Smoking Cessation Community to Identify Smoking Status: A Machine Learning Approach.

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Journal:  Decis Support Syst       Date:  2018-10-15       Impact factor: 5.795

8.  Examining Thematic Similarity, Difference, and Membership in Three Online Mental Health Communities from Reddit: A Text Mining and Visualization Approach.

Authors:  Albert Park; Mike Conway; Annie T Chen
Journal:  Comput Human Behav       Date:  2017-09-06

9.  Determining the prevalence of cannabis, tobacco, and vaping device mentions in online communities using natural language processing.

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Journal:  Drug Alcohol Depend       Date:  2021-09-06       Impact factor: 4.492

10.  Insulin pricing and other major diabetes-related concerns in the USA: a study of 46 407 tweets between 2017 and 2019.

Authors:  Adrian Ahne; Francisco Orchard; Xavier Tannier; Camille Perchoux; Beverley Balkau; Sherry Pagoto; Jessica Lee Harding; Thomas Czernichow; Guy Fagherazzi
Journal:  BMJ Open Diabetes Res Care       Date:  2020-06
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