Intentional analysis of medical conversations for community engagement

With an explosion in the proliferation of user-generated content in communities, information overload is increasing and quality of readily available online content is deteriorating. There is an increasing need for intelligent systems that make use of implicit user-generated knowledge in communities for community engagement. We describe our approach based on modeling user utterances in communities to proactively target the community for exchange of questions and answers. We envision a system that automatically encourages user engagement and participation by routing relevant conversations to users based on individual and community activity levels.

In this paper, we analyze health forum conversations from WebMD, a popular health portal consumer site, and classify them in different acts of speech using Verbal Response Modes (VRM) theory. We describe our approach for modeling an intelligent community recommender to engage participants based on observations from our analysis.

Read the paper:

Intentional analysis of medical conversations for community engagement

by Saurav Sahay, Hua Ai, Ashwin Ram

FLAIRS-11 International Conference on Artificial Intelligence
www.cc.gatech.edu/faculty/ashwin/papers/er-11-01.pdf
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