Journal article
Mining user-generated content in an online smoking cessation community to identify smoking status: A machine learning approach
Decision Support Systems, Vol.116, pp.26-34
01/2019
DOI: 10.1016/j.dss.2018.10.005
PMCID: PMC6934371
PMID: 31885411
Abstract
Online smoking cessation communities help hundreds of thousands of smokers quit smoking and stay abstinent each year. Content shared by users of such communities may contain important information that could enable more effective and personally tailored cessation treatment recommendations. This study demonstrates a novel approach to determine individuals' smoking status by applying machine learning techniques to classify user-generated content in an online cessation community. Study data were from BecomeAnEX.org, a large, online smoking cessation community. We extracted three types of novel features from a post: domain-specific features, author-based features, and thread-based features. These features helped to improve the smoking status identification (quit vs. not) performance by 9.7% compared to using only text features of a post's content. In other words, knowledge from domain experts, data regarding the post author's patterns of online engagement, and other community member reactions to the post can help to determine the focal post author's smoking status, over and above the actual content of a focal post. We demonstrated that machine learning methods can be applied to user-generated data from online cessation communities to validly and reliably discern important user characteristics, which could aid decision support on intervention tailoring.
•Analyze large-scale user-generated data in an online health community for behavior change•Identify individual authors' smoking status (quit vs not) with machine learning•Include novel features beyond content of focal posts that improve classifier performance•Incorporate technical innovations based upon insights from domain experts
Details
- Title: Subtitle
- Mining user-generated content in an online smoking cessation community to identify smoking status: A machine learning approach
- Creators
- Xi Wang - School of Information, Central University of Finance and Economics, Beijing, ChinaKang Zhao - Tippie College of Business, The University of Iowa, Iowa City, IA, United States of AmericaSarah Cha - Schroeder Institute, Truth Initiative, Washington, DC, United States of AmericaMichael S Amato - Schroeder Institute, Truth Initiative, Washington, DC, United States of AmericaAmy M Cohn - Schroeder Institute, Truth Initiative, Washington, DC, United States of AmericaJennifer L Pearson - Schroeder Institute, Truth Initiative, Washington, DC, United States of AmericaGeorge D Papandonatos - Center for Statistical Sciences, Brown University, Providence, RI, United States of AmericaAmanda L Graham - Schroeder Institute, Truth Initiative, Washington, DC, United States of America
- Resource Type
- Journal article
- Publication Details
- Decision Support Systems, Vol.116, pp.26-34
- DOI
- 10.1016/j.dss.2018.10.005
- PMID
- 31885411
- PMCID
- PMC6934371
- NLM abbreviation
- Decis Support Syst
- ISSN
- 0167-9236
- eISSN
- 1873-5797
- Publisher
- Elsevier B.V
- Grant note
- DOI: 10.13039/100000002, name: National Institutes of Health, award: R01 CA192345
- Language
- English
- Date published
- 01/2019
- Academic Unit
- Business Analytics
- Record Identifier
- 9984083851802771
Metrics
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