Expertise networks in online communities
University of Michigan–Ann Arbor
Abstract
Web-based communities have become important places for people to seek and share expertise. We find that networks in these communities typically differ in their topology from other online networks such as the World Wide Web. Systems targeted to augment web-based communities by automatically identifying users with expertise, for example, need to adapt to the underlying interaction dynamics. In this study, we analyze the Java Forum, a large online help-seeking community, using social network analysis methods. We test a set of network-based ranking algorithms, including PageRank and HITS, on this large size social network in order to identify users with high expertise. We then use simulations to identify a small…
Citation impact
- FWCI
- 121.77
- Percentile
- 100%
- References
- 29
Authors
3Topics & keywords
- Computer science
- PageRank
- Java
- Ranking (information retrieval)
- Set (abstract data type)
- World Wide Web
- Social network (sociolinguistics)
- Data science