Hubs, authorities, and communities
ACM Computing Surveys (CSUR)
Expertise networks in online communities: structure and algorithms
Proceedings of the 16th international conference on World Wide Web
Knowledge sharing and yahoo answers: everyone knows something
Proceedings of the 17th international conference on World Wide Web
Questions in, knowledge in?: a study of naver's question answering community
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Power-Law Distributions in Empirical Data
SIAM Review
How do programmers ask and answer questions on the web? (NIER track)
Proceedings of the 33rd International Conference on Software Engineering
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
An empirical study on developer interactions in StackOverflow
Proceedings of the 28th Annual ACM Symposium on Applied Computing
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In this paper, we analyze the network of expertise constructed from the interactions of users on the online question-answering (QA) community of Stack Overflow. This community was built with the intention of helping users with their programming tasks and, thus, questions are expected to be highly factual. This also indicates that the answers one provides may be highly indicative of one's level of expertise on the subject matter. Therefore, our main concern is how to model and characterize the user's expertise based on the constructed network and its centrality measures. We used the user's reputation established on Stack Overflow as a direct proxy to their expertise. We further made use of linear models and principal component analysis for the purpose. We found out that the current reputation system does a decent job at representing the user's expertise and that focus matters when answering factual questions. However, our model was not able to capture the other larger half of reputation which is specifically designed to reflect a user's trustworthiness besides their expertise. Along the way, we also discovered facts that have been known in earlier studies of the other/same QA communities such as the power-law degree distribution of the network and the generalized reciprocity pattern among its users.