Classification of Online Health Discussions with Text and Health Feature Sets

Nowadays, many health groups and forums are established on the Internet, where health consumers discuss health issues and interact with each other. Although there is a large amount of user generated content about healthcare on different social media sites, few studies have applied data mining or artificial intelligence techniques for knowledge discovery on a large scale of data in this particular emerging area. In online health forums, it is difficult for users to find relevant topics or peers due to the large amount of information. Traditional recommendation systems may not work well for health online forums, because health consumers have different intentions of participation or may be interest in different types of supports even if the content matches their interest. To help solving this problem, we apply Naïve Bayes methods in this study to classify posts and comments on QuitStop forum, which is an online community for smoking cessation intervention. Classifiers are built on different text features and health features of user quit status. Two different classification tasks are investigated: (1) classification of user intentions, and (2) classification of types of social support exchanged in interactions. We developed classifiers for posts and comments separately, and conducted experiments to compare classifiers with different text and health feature sets. It is found that using thread title or post content can achieve the highest classification accuracy on both posts and comments for user intention classification with text features. On the other hand, using the content of post or comment itself performs the best for the classification of social support types. In particular for the post, integrating health features of the post author can boost the text classifications of user intention and support type. However, user health features cannot help in improving text classifiers for the comments.