Conflict detection in online textual health advice: demo abstract

Textual health advice generated from different online sources (e.g., health apps and websites) can be conflicting. Conflicts can occur due to lexical features, (such as, negation, antonyms, or numerical mismatch) or can be conditioned upon time and/or physiological status. Detecting conflicts from textual health advice poses several challenges, including, large structural variation between text and hypothesis pairs, finding conceptual overlap between pairs of advice, and inference of the semantics of an advice (i.e., what to do, why, and how). In this demonstration, we present a semantic rule-based system to detect different types of conflicts in online textual health advice statements in a context-aware and interpretable manner.