An Interests Discovery Approach in Social Networks Based on Semantically Enriched Graphs
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Finding topic-level experts in scholarly networks
Scientometrics
| Hi-index | 0.00 |
As computer-mediated communication services in the Web 2.0 arena, online communities have become very important places for Web users to share knowledge and experiences. One important research issue in online communities is how to find expert users in the community. In this paper, we investigate the expertise that users displayed in online communities, especially in discussion groups and propose an effective expert ranking algorithm, which integrates both discussion thread contents and social network extracted from massive social interactions. We present a vector space model to compute the content relevance part and a PageRank style algorithm for the expert network part. Considering the expert spamming issue caused by mutually referencing in a small group, we modify the original PageRank algorithm and propose a novel ranking algorithm. The two parts are lastly integrated using a cascade strategy. The experimental results show that our so-called ExpertRank algorithm is an effective expert ranking algorithm, which can guarantee that the highly ranked experts are both highly relevant to the specific queries and highly authoritative in corresponding areas.