InfluenceRank: Trust-based influencers identification using social network analysis in Q&A sites

Abstract

Question and Answering (Q&A) sites are recently gaining popularity on the Web. People using such sites are like a communityanyone can ask, anyone can answer, and everyone can share, since all of the questions and answers are public and searchable immediately. This mechanism can reduce the time and effort to find the most relevant answer. Unfortunately, the users suffer from answer quality problem due to several reasons including limited knowledge about the question domain, bad intentions (e.g. spam, making fun of others), limited time to prepare good answers, etc. In order to identify the credible users to help people find relevant answer, in this paper, we propose a ranking algorithm, InfluenceRank, which is basis of analyzing relationship in terms of users' activities and their mutual trusts. Our experimental studies show that the proposed algorithm significantly outperforms the baseline algorithms. Copyright © 2012 The Institute of Electronics, Information and Communication Engineers.