Shah Adnan Muhammad, Yan Xiangbin, Qayyum Abdul
Department of Management Science and Engineering, School of Management, Harbin Institute of Technology, Harbin, China.
School of Economics and Management, University of Science and Technology Beijing, Beijing, China.
Healthc Inform Res. 2021 Apr;27(2):116-126. doi: 10.4258/hir.2021.27.2.116. Epub 2021 Apr 30.
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.
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).
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%.
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.
用户通过在线戒烟社区（OSCCs）分享有价值的信息，这些社区有助于人们维持和改善戒烟行为。尽管OSCC的使用在吸烟者中很普遍，但在识别OSCC用户的吸烟状态（“已戒烟”与“未戒烟”）方面存在局限性。因此，本研究通过先进的计算方法和来自一个OSCC的真实数据，对用户生成内容（UGC）进行了隐含分析，以识别个体用户的吸烟状态。
使用来自BcomeAnEX.org的3833名用户的数据进行二次数据分析。领域专家审查帖子和评论，以确定作者撰写时的吸烟状态。从UGC中提取了七种类型的特征集（文本、Doc2Vec、社会影响、特定领域、基于作者、基于主题的特征以及相邻帖子）。
与仅使用文本帖子特征相比，引入新特征使吸烟状态识别（已戒烟与未戒烟）提高了9.3%。此外，在所有模型中，先进的计算方法均优于基线算法，吸烟状态预测性能提高了12%。
本研究结果表明，当前的研究方法为参与在线戒烟干预的研究人员提供了一个有价值的平台，并为正在进行的机器学习应用提供了一个框架。这些结果可能有助于从业者通过在OSCC中进行个性化帖子推荐来设计可持续的实时干预措施。一个主要限制是仅检测了用户的吸烟状态。未来的研究可能涉及编程机器学习分类方法，以使用更大的数据集识别戒烟持续时间。
