A personalized credibility model for recommending messages in social participatory media environments

We propose a method to determine the credibility of messages that are posted in participatory media (such as blogs), of use in recommender systems designed to provide users with messages that are considered to be the most credible to them. Our approach draws from theories developed in sociology, political science, and information science-we show that the social context of users influences their opinion about the credibility of messages they read, and that this context can be captured by analyzing the social network of users. We use this insight to improve recommendation algorithms for messages created in participatory media environments. Our methodology rests on Bayesian learning, integrating new concepts of context and completeness of messages inspired by the strength of weak ties hypothesis from social network theory. We show that our credibility evaluation model can be used to significantly enhance the performance of collaborative filtering recommendation. Experimental validation is done using datasets obtained from social networking websites used for knowledge sharing. We conclude by clarifying our relationship to the semantic adaptive social web, emphasizing our use of personal evaluations of messages and the social network of users, instead of merely automated semantic interpretation of content.