Objectives: Users share valuable information through online smoking cessation communities (OSCCs), which help people maintain and improve smoking cessation behavior. Although OSCC utilization is common among smokers, limitations exist in identifying the smoking status of OSCC users (“quit” vs. “not quit”). Thus, the current study implicitly analyzed user-generated content (UGC) to identify individual users’ smoking status through advanced computational methods and real data from an OSCC. Methods: Secondary data analysis was conducted using data from 3,833 users of BcomeAnEX.org. Domain experts reviewed posts and comments to determine the authors’ smoking status when they wrote them. Seven types of feature sets were extracted from UGC (textual, Doc2Vec, social influence, domain-specific, author-based, and thread-based features, as well as adjacent posts). Results: Introducing novel features boosted smoking status recognition (quit vs. not quit) by 9.3% relative to the use of text-only post features. Furthermore, advanced computational methods outperformed baseline algorithms across all models and increased the smoking status prediction performance by up to 12%. Conclusions The results of this study suggest that the current research method provides a valuable platform for researchers involved in online cessation interventions and furnishes a framework for on-going machine learning applications. The results may help practitioners design a sustainable real-time intervention via personalized post recommendations in OSCCs. A major limitation is that only users’ smoking status was detected. Future research might involve programming machine learning classification methods to identify abstinence duration using larger datasets.

Objectives: Users share valuable information through online smoking cessation communities (OSCCs), which help people maintain and improve smoking cessation behavior. Although OSCC utilization is common among smokers, limitations exist in identifying the smoking status of OSCC users (“quit” vs. “not quit”). Thus, the current study implicitly analyzed user-generated content (UGC) to identify individual users’ smoking status through advanced computational methods and real data from an OSCC. Methods: Secondary data analysis was conducted using data from 3,833 users of BcomeAnEX.org. Domain experts reviewed posts and comments to determine the authors’ smoking status when they wrote them. Seven types of feature sets were extracted from UGC (textual, Doc2Vec, social influence, domain-specific, author-based, and thread-based features, as well as adjacent posts). Results: Introducing novel features boosted smoking status recognition (quit vs. not quit) by 9.3% relative to the use of text-only post features. Furthermore, advanced computational methods outperformed baseline algorithms across all models and increased the smoking status prediction performance by up to 12%. Conclusions The results of this study suggest that the current research method provides a valuable platform for researchers involved in online cessation interventions and furnishes a framework for on-going machine learning applications. The results may help practitioners design a sustainable real-time intervention via personalized post recommendations in OSCCs. A major limitation is that only users’ smoking status was detected. Future research might involve programming machine learning classification methods to identify abstinence duration using larger datasets.

COVID-19/virology* ; Cities ; Environmental Monitoring ; Genome, Viral ; Humans ; Infection Control/methods* ; Pakistan ; RNA, Viral/genetics* ; SARS-CoV-2/genetics* ; Sewage/virology*