People on Drugs: Credibility of User Statements in Health Communities

Online health communities are a valuable source of information for patients and physicians. However, such user-generated 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.

Publications

  • Subhabrata Mukherjee, Gerhard Weikum and Cristian Danescu-Niculescu-Mizil.
    People on Drugs: Credibility of User Statements in Health Communities.
    Proc. of the 20th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD). 2014.
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Health forum dataset used in the KDD 2014 paper: