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

TL;DR

This paper introduces a scalable probabilistic model combining topic modeling and community detection to analyze user roles and interactions in large online forums, revealing both micro and macro-level structures.

Contribution

The authors develop a novel scalable algorithm integrating LDA and MMSB models for uncovering subcommunity structures and user roles in massive online discussion datasets.

Findings

Model effectively explains user presentation characteristics using discovered topics.

Outperforms MMSB and LDA in predicting reply structures.

Synthetic experiments show high stability and accurate parameter recovery.

Abstract

Online discussion forums are complex webs of overlapping subcommunities (macrolevel structure, across threads) in which users enact different roles depending on which subcommunity they are participating in within a particular time point (microlevel structure, within threads). This sub-network structure is implicit in massive collections of threads. To uncover this structure, we develop a scalable algorithm based on stochastic variational inference and leverage 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(15.1K users and 12.4K threads) and StackOverFlow (1.19 million users and 4.55 million threads). Qualitatively, we demonstrate that our model can provide useful explanations of microlevel and macrolevel user presentation characteristics in different communities using the topics discovered from posts. Quantitatively, we show that our model does better than MMSB and LDA in predicting user reply structure within threads. In addition, we demonstrate via synthetic data experiments that the proposed active sub-network discovery model is stable and recovers the original parameters of the experimental setup with high probability.