2016/10/18 by Teague Henry, Teague R. Henry, David Banks +7 · 1 citation
Computer Science · Mathematics · Physics and Astronomy · #Complex Network Analysis Techniques #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (stat.ML) #Opinion Dynamics and Social Influence #Physics and Society (physics.soc-ph) #Social and Information Networks (cs.SI) #Topic Modeling #cs.SI #physics.soc-ph #stat.ML
paper · pdf · doi:10.48550/arxiv.1610.05756
Accepted at Journal of Classification
openalex publication_date 2016/10/18 · arxiv created 2018/08/23 · arxiv updated 2018/08/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The last decade has seen great progress in both dynamic network modeling and topic modeling. This paper draws upon both areas to create a Bayesian method that allows topic discovery to inform the latent network model and the network structure to facilitate topic identification. We apply this method to the 467 top political blogs of 2012. Our results find complex community structure within this set of blogs, where community membership depends strongly upon the set of topics in which the blogger is interested.