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Large Question-and-Answer (Q&A) platforms support diverse knowledge curationon the Web. While researchers have studied user behavior on the platforms in avariety of contexts, there is relatively little insight into importantby-products of user behavior that also encode knowledge. Here, we analyze andmodel the macroscopic structure of tags applied by users to annotate andcatalog questions, using a collection of 168 Stack Exchange websites. We findstriking similarity in tagging structure across these Stack Exchangecommunities, even though each community evolves independently (albeit undersimilar guidelines). Using our empirical findings, we develop a simplegenerative model that creates random bipartite graphs of tags and questions.Our model accounts for the tag frequency distribution but does not explicitlyaccount for co-tagging correlations. Even under these constraints, wedemonstrate empirically and theoretically that our model can reproduce a numberof statistical properties of the co-tagging graph that links tags appearing inthe same post.

Modeling and Analysis of Tagging Networks in Stack Exchange Communities