Online health communities are a valuable source of information for patients and physicians. However, such usergenerated resources are often plagued by inaccuracies and misinformation. In this work we propose a method for automatically establishing the credibility of user-generated medical statements and the trustworthiness of their authors by exploiting linguistic cues and distant supervision from expert sources. To this end we introduce a probabilistic graphical model that jointly learns user trustworthiness, statement credibility, and language objectivity. We apply this methodology to the task of extracting rare or unknown side-effects of medical drugs-this being one of the problems where large scale non-expert data has the potential to complement expert medical knowledge. We show that our method can reliably extract side-effects and filter out false statements, while identifying trustworthy users that are likely to contribute valuable medical information.
Credibility analysis within social media: REF proposes a probabilistic graphical model jointly inferring user trustworthiness, language objectivity, and statement credibility.
People on drugs: credibility of user statements in health communities
KDD '14
Computer Science Mathematics