HealthTvizer: Exploring Health Awareness in Twitter Data through Coordinated Multiple Views

Analyzing public user posts and shared information on social media can assist us in measuring various population characteristics, patterns, movements, and as well as the public health conditions. In recent years, researchers have been analyzing social media (such as Facebook or Twitter feeds) to detect and predict various emerging events and market trends. Fewer attentions have been paid to the epidemic of the diseases. In this paper, we present a social media analytics tool, called HealthTvizer, for exploring health awareness using Twitter data through interactive and interconnected multiple views. We use topic modeling to pick the relevant and meaningful terms from more than 57 million tweets. We detect the disease name and related contents which are shared by the users of different geographical locations (mostly in the United States). We believe that the collected geolocations from the users' tweets can reveal the patterns of diseases for a given term which allows a researcher to detect, analyze, and explore information about the diseases and hence take necessary steps to improve public health awareness. We validate the effectiveness of HealthTvizer through an informal user study. The feedback from this study also motivates us on interesting future extensions of the tool.