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We present the Bayesian Echo Chamber, a new Bayesian generative model forsocial interaction data. By modeling the evolution of people's language usageover time, this model discovers latent influence relationships between them.Unlike previous work on inferring influence, which has primarily focused onsimple temporal dynamics evidenced via turn-taking behavior, our model capturesmore nuanced influence relationships, evidenced via linguistic accommodationpatterns in interaction content. The model, which is based on a discrete analogof the multivariate Hawkes process, permits a fully Bayesian inferencealgorithm. We validate our model's ability to discover latent influencepatterns using transcripts of arguments heard by the US Supreme Court and themovie "12 Angry Men." We showcase our model's capabilities by using it to inferlatent influence patterns from Federal Open Market Committee meetingtranscripts, demonstrating state-of-the-art performance at uncovering socialdynamics in group discussions.

The Bayesian Echo Chamber: Modeling Social Influence via Linguistic Accommodation