ArticleDecision support systems2019
Mining User-Generated Content in an Online Smoking Cessation Community to Identify Smoking Status: A Machine Learning Approach.
Article in Decision support systems, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- Time Out: A Scoping Review of Non-Duration Based Social Media Use Measures and Adolescent Mental Health.Journal of adolescence · 2026Article
- Exploring the Incentive Function of Virtual Academic Degrees in a Chinese Online Smoking Cessation Community: Qualitative Content Analysis.Journal of medical Internet research · 2023Article
- Applied Artificial Intelligence for Tobacco Cessation in the Era of COVID-19: A Perspective.Asian Pacific journal of cancer prevention : APJCP · 2022Article
- User Behaviors and User-Generated Content in Chinese Online Health Communities: Comparative Study.Journal of medical Internet research · 2021Article
- Determining the prevalence of cannabis, tobacco, and vaping device mentions in online communities using natural language processing.Drug and alcohol dependence · 2021Article
- Application of Automated Text Analysis to Examine Emotions Expressed in Online Support Groups for Quitting Smoking.Journal of the Association for Consumer Research · 2021Article
- Social Network Analysis of an Online Smoking Cessation Community to Identify Users' Smoking Status.Healthcare informatics research · 2021Article
- Social Media as a Research Tool (SMaaRT) for Risky Behavior Analytics: Methodological Review.JMIR public health and surveillance · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
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.
Indexed as
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.