Classifying User Intention and Social Support Types in Online Healthcare Discussions

With the development of online healthcare social media, a large volume of user generated content are available in different health discussion forums or groups. Healthcare social media sites could empower patients to play a substantial role in their treatment by acquiring knowledge and support through actively involving in online discussions and interactions. However, users may have difficulty to find relevant topics or peers in these online health forums with a large amount of unstructured information. Most recommendation systems rely on content-based approach to recommend peers or discussions to their participants. However, in healthcare social media sites, content-based approach is not sufficient because health consumers may have different intentions of participation or may be interest in different types of support even if the content matches their interest. Based on previous studies, we utilize Naïve Bayes methods and propose two tasks for classifying posts and comments on Quit Stop forum, an online community for smoking cessation intervention, respectively: (1) classification of intentions and (2) classification of social support types. Different text feature sets and user health feature sets are selected to develop classifiers. Taking different evaluation indicators as optimizing goals, we develop genetic algorithms to combine classifiers with different feature sets and optimize the classification results. It is found that for post classification, integrating text and health features could achieve the highest precision, recall and F1 measure. For comment classification, combining different text features could reach the best result. In the future, the classification result could be applied to developing recommender systems for topic recommendation and user prediction of online health forums.