{"type":"ScholarlyArticle","title":"A Murder and Protests, the Capitol Riot, and the Chauvin Trial: Estimating Disparate News Media Stance","authors":[{"name":"Sujan Dutta","slug":"sujan-dutta","given":"Sujan","family":"Dutta","affiliation":"Rochester Institute of Technology"},{"name":"Beibei Li","slug":"beibei-li","given":"Beibei","family":"Li","affiliation":"Carnegie Mellon University"},{"name":"Daniel S. Nagin","slug":"daniel-s-nagin","given":"Daniel S.","family":"Nagin","affiliation":"Carnegie Mellon University"},{"name":"Ashiqur R. KhudaBukhsh","slug":"ashiqur-r-khudabukhsh","given":"Ashiqur R.","family":"KhudaBukhsh","affiliation":"Rochester Institute of Technology"}],"abstract":"In this paper, we analyze the responses of three major US cable news networks to three seminal policing events in the US spanning a thirteen month period--the murder of George Floyd by police officer Derek Chauvin, the Capitol riot, Chauvin's conviction, and his sentencing. We cast the problem of aggregate stance mining as a natural language inference task and construct an active learning pipeline for robust textual entailment prediction. Via a substantial corpus of 34,710 news transcripts, our analyses reveal that the partisan divide in viewership of these three outlets reflects on the network's news coverage of these momentous events. In addition, we release a sentence-level, domain-specific text entailment data set on policing consisting of 2,276 annotated instances.","doi":"10.24963/ijcai.2022/702","url":"https://ijcai.org/proceedings/2022/702","pdf":"https://ijcai.org/proceedings/2022/0702.pdf","markdown":"https://ijcai.org/proceedings/2022/702.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/702","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Research Papers","track":"AI for Good","number":702,"pages":{"first":5059,"last":5065},"keywords":["Multidisciplinary Topics and Applications: Social Sciences","Multidisciplinary Topics and Applications: News and Media","Machine Learning: Active Learning"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38987138","length_sec":80,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38985281","length_sec":648,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Bender et al., 2021] Emily M. 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