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.
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