{"id":215394,"date":"2015-05-01T00:00:00","date_gmt":"2015-05-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/the-bayesian-echo-chamber-modeling-social-influence-via-linguistic-accommodation\/"},"modified":"2018-10-16T22:04:30","modified_gmt":"2018-10-17T05:04:30","slug":"the-bayesian-echo-chamber-modeling-social-influence-via-linguistic-accommodation","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/the-bayesian-echo-chamber-modeling-social-influence-via-linguistic-accommodation\/","title":{"rendered":"The Bayesian Echo Chamber: Modeling Social Influence via Linguistic Accommodation"},"content":{"rendered":"<div class=\"asset-content\">\n<p>We present the Bayesian Echo Chamber, a new Bayesian generative model for social interaction data. By modeling the evolution of people\u2019s language usage over time, this model discovers latent influence relationships between them. Unlike previous work on inferring influence, which has primarily focused on simple temporal dynamics evidenced via turn-taking behavior, our model captures more nuanced influence relationships, evidenced via linguistic accommodation patterns in interaction content. The model, which is based on a discrete analog of the multivariate Hawkes process, permits a fully Bayesian inference algorithm. We validate our model\u2019s ability to discover latent influence patterns using transcripts of arguments heard by the US Supreme Court and the movie \u201c12 Angry Men.\u201d We showcase our model\u2019s capabilities by using it to infer latent influence patterns from Federal Open Market Committee meeting transcripts, demonstrating state-of-the-art performance at uncovering social dynamics in group discussions.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>We present the Bayesian Echo Chamber, a new Bayesian generative model for social interaction data. By modeling the evolution of people\u2019s language usage over time, this model discovers latent influence relationships between them. Unlike previous work on inferring influence, which has primarily focused on simple temporal dynamics evidenced via turn-taking behavior, our model captures more [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"","msr_conference_name":"Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics","msr_doi":"","msr_arxiv_id":"","msr_s2_paper_id":"","msr_mag_id":"","msr_pubmed_id":"","msr_other_authors":"F. Guo, C. Blundell, H. Wallach, K. 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