Abstract
The development and spread of social media services have made it possible for new approaches to be used to survey the public and society. One popular application is health surveillance, that is, predicting disease epidemics and symptoms from texts on social media services. In this paper, we address an application of natural language processing for detecting an episode of a disease/symptom (e.g., flu and cold) in social media texts. Following an error analysis of the state-of-the-art system, we identified two important and generic subtasks for improving the accuracy of the system: factuality analysis and subject identification. We address these subtasks and demonstrate their impact on detecting an episode of a disease/symptom.
