Analyzing Perceived Intentions of Public Health-Related Communication on Twitter

The increasing population with chronic diseases and highly engaged in online communication has triggered an urge in healthcare to understand this phenomenon. We propose an automatic approach to analyze the perceived intentions behind public tweets. Our long-term goal is to create high-level, behavioral models of the health information consumers and disseminators, relevant to studies in narrative medicine and health information dissemination. The contributions of this paper are: (1) a validated intention taxonomy, derived from pragmatics and empirically adjusted to Twitter public communication; (2) a tagged health-related corpus of 1100 tweets; (3) an effective approach to automatically discover intentions from text, using supervised machine learning with discourse features only, independent of domain vocabulary. Reasoning on the results, we claim the transferability of our solution to other healthcare corpora, enabling thus more extensive studies in the concerned domains.