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Online discussion forums are complex webs of overlapping subcommunities(macrolevel structure, across threads) in which users enact different rolesdepending on which subcommunity they are participating in within a particulartime point (microlevel structure, within threads). This sub-network structureis implicit in massive collections of threads. To uncover this structure, wedevelop a scalable algorithm based on stochastic variational inference andleverage topic models (LDA) along with mixed membership stochastic block (MMSB)models. We evaluate our model on three large-scale datasets,Cancer-ThreadStarter (22K users and 14.4K threads), Cancer-NameMention(15.1Kusers and 12.4K threads) and StackOverFlow (1.19 million users and 4.55 millionthreads). Qualitatively, we demonstrate that our model can provide usefulexplanations of microlevel and macrolevel user presentation characteristics indifferent communities using the topics discovered from posts. Quantitatively,we show that our model does better than MMSB and LDA in predicting user replystructure within threads. In addition, we demonstrate via synthetic dataexperiments that the proposed active sub-network discovery model is stable andrecovers the original parameters of the experimental setup with highprobability.

Scalable Modeling of Conversational-role based Self-presentation Characteristics in Large Online Forums