Despite echo chambers in social media have been under considerable scrutiny, general models for their detection and analysis are missing. In this work, we aim to fill this gap by proposing a probabilistic generative model that explains social media footprints -- i.e., social network structure and propagations of information -- through a set of latent communities, characterized by a degree of echo-chamber behavior and by an opinion polarity. Specifically, echo chambers are modeled as communities that are permeable to pieces of information with similar ideological polarity, and impermeable to information of opposed leaning: this allows discriminating echo chambers from communities that lack a clear ideological alignment. To learn the model parameters we propose a scalable, stochastic adaptation of the Generalized Expectation Maximization algorithm, that optimizes the joint likelihood of observing social connections and information propagation. Experiments on synthetic data show that our algorithm is able to correctly reconstruct ground-truth latent communities with their degree of echo-chamber behavior and opinion polarity. Experiments on real-world data about polarized social and political debates, such as the Brexit referendum or the COVID-19 vaccine campaign, confirm the effectiveness of our proposal in detecting echo chambers. Finally, we show how our model can improve accuracy in auxiliary predictive tasks, such as stance detection and prediction of future propagations.

The ISI Foundation is looking for exceptional candidates to join the Algorithmic Data Analytics research team, under the supervision of Dr. Francesco Bonchi . The appointed researcher will undertake fundamental research activities related to the development of methods and algorithms for data science. Talented and highly motivated postdoctoral researchers, as well as more senior researchers with internationally-recognized achievements, will be considered. Core skills you will need: - Ph.D. in Computer Science or related disciplines, with special emphasis on one of the following areas: Data Mining, Machine Learning, Algorithmic Theory, High Performance (Data-intensive, Scalable, Distributed, Streaming) Computing, Data Bases. ISI offers: - We provide an unusually rich opportunity for collegial interaction in a highly competitive and multidisciplinary environment.