0000000000237764
AUTHOR
Konstantinos Pelechrinis
Automatic evaluation of information provider reliability and expertise
Published version of an article in the journal: World Wide Web. Also available from the publisher at: http://dx.doi.org/10.1007/s11280-013-0249-x 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 (e.g., asynchronous and convenient access). 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 (reliability and expertise) significantly affect the quality o…
A Cognitive-based scheme for user reliability and expertise assessment in Q&A social networks
Q&A social media has gained a great deal of attention during recent years. People rely on these sites to obtain information due to the number of advantages they offer as compared to conventional sources of knowledge (e.g., asynchronous and convenient access). However, for the same question one may find highly contradictory answers, causing ambiguity with respect to the correct information. This can be attributed to the presence of unreliable and/or non-expert users. In this work, we propose a novel approach for estimating the reliability and expertise of a user based on human cognitive traits. Every user can individually estimate these values based on local pairwise interactions. We examine…
Collaborative Assessment of Information Provider's Reliability and Expertise Using Subjective Logic
QA each user can individually estimate the expertise and the reliability of her peers using her direct interactions with them and our framework. 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. We emulate a Q&A SN to examine various performance aspects of our algorithm (e.g., convergence time, responsiveness etc.). Our evaluations indicate that it can accurately assess the reliability and the expertise of a user with a small number of samples and can successfully react to the latter's behavior change, provided that the cognitive traits hold in practice.