Fine-Grained Information Identification in Health Related Posts

Online health communities have become a medium for patients to share their personal experiences and interact with peers on topics related to a disease, medication, side effects, and therapeutic processes. Analyzing informational posts in these communities can provide an insightful view about the dominant health issues and can help patients find the information that they need easier. In this paper, we propose a computational model that mines user content in online health communities to detect positive experiences and suggestions on health improvement as well as negative impacts or side effects that cause suffering throughout fighting with a disease. Specifically, we combine high-level, abstract features extracted from a convolutional neural network with lexicon-based features and features extracted from a long short term memory network to capture the semantics in the data. We show that our model, with and without lexicon-based features, outperforms strong baselines.