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In this paper we present a methodology for gaining a better understanding of the contribution behavior, interests and expertise of communities of Wikipedia users. Starting from a list of core articles and their main editors, we identify which other articles (outside of the initial list) they contributed to ‘significantly’. The ordering is based on (empirical) Bayesian estimates of the contribution probabilities for each of the articles. By constructing a co-contribution network, we can identify the general themes the community expresses exceptional interest (or disinterest) in. In order to show what type of insights one might gain from employing the proposed method, we use the editors that contributed to the articles on designer drugs as a case study. We find that the users in this community contribute significantly to articles on pharmaceuticals, popular party drugs, chemistry, mental illnesses, diseases, medicine and cell biology

From MDMA to Lady Gaga: Expertise and Contribution Behavior of Editing Communities on Wikipedia