Collaborative Assessment of Information Provider's Reliability and Expertise Using Subjective Logic

Q&A social media have gained a lot of attention during the recent years. People rely on these sites to obtain information due to a number of advantages they offer as compared to conventional sources of knowledge. However, for the same question one may find highly contradicting answers, causing an ambiguity with respect to the correct information. This can be attributed to the presence of unreliable and/or non-expert users. These two attributes significantly affect the quality of the answer provided. We present a novel approach for estimating these user’s characteristics relying on human cognitive traits. In brief, we propose each user to monitor the activity of her peers and observe their compliance with predefined cognitive models. These observations lead to local assessments that can be further fused using subjective logic to obtain a reliability and expertise consensus for every other user in the social network (SN). To the best of our knowledge this is the first study of this kind in the context of Q&A SN. Our proposed approach is highly distributed; each user can individually estimate the expertise and the reliability of her peers. The online SN(OSN), which can be considered as a distributed database, performs continuous data aggregation for users expertise and reliability assessment in order to reach a consensus. Our evaluations indicate that it can accurately assess the reliability and the expertise of a user and can successfully react to the latter’s behavior change.