Online discussions evolve through cascades of interactions that mirror collective opinion dynamics. This work introduces a probabilistic generative model that infers latent communities based on their degree of echo-chamber behavior and ideological polarity. By learning from both network structure and information propagation, the model identifies how polarized subgroups emerge and interact, providing a scalable statistical tool for understanding political and social fragmentation online.

Social norms regulate collective behavior, and social media offers a large-scale lens to observe how these norms vary across groups. Using Reddit’s r/AITA community, this work analyzes how demographic factors shape moral judgments and perceptions of deviance. It finds systematic biases by gender and age, showing how online norm enforcement both mirrors and transforms the moral organization of society.

Understanding ideology in social media requires models that learn from how information propagates, not just what it contains. This paper introduces a stochastic generative model that infers multidimensional ideological embeddings by fitting the flow of politically salient content across users. By capturing alignment through diffusion dynamics rather than labels, the model learns the structural complexity of political belief systems, offering a principled framework for representing ideology in networked communication.