Finding credible information sources in social networks based on content and social structure
A task of primary importance for social network users is to decide whose updates to subscribe to in order to maximize the relevance, credibility, and quality of the information received. To address this problem, we conducted an experiment designed to measure the extent to which different factors in online social networks affect both explicit and implicit judgments of credibility. The results of the study indicate that both the topical content of information sources and social network structure affect source credibility. Based on these results, we designed a novel method of automatically identifying and ranking social network users according to their relevance and expertise for a given topic. We performed empirical studies to compare a variety of alternative ranking algorithms and a proprietary service provided by a commercial website specifically designed for the same purpose. Our findings show a great potential for automatically identifying and ranking credible users for any given topic.
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Canini, K.; Suh, B.; Pirolli, P. L. Finding credible information sources in social networks based on content and social structure. Third IEEE International Conference on Social Computing (SocialCom); 2011 October 9-11; Boston, MA.
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