Confluence of social network, social question and answering community, and user reputation model for information seeking and experts generation

Abstract

Social question and answering (Q&A) is one of the most effective approaches to knowledge acquisition using information seeking and collaboration. Most modern social Q&A systems use a static points-based user reputation model, which has the effect of diminishing the value of experts. In order to overcome this issue, we have developed a dynamic points-based user reputation model that takes user rating and social network analysis as input. The impact weight of each relation and user ratings are not static but are dependent on the current level of asker and answerer and on the difficulty level of the question. We propose a novel social Q&A platform that is the confluence of different features of social network, social Q&A, and the dynamic points-based user reputation model. The beta version of the system was evaluated by conducting a clinical study for 4 months in different academic environments. The results show that the proposed social Q&A outperforms the available static points-based social Q&A systems in representing the actual user reputation with an increased user satisfaction.