A Murder and Protests, the Capitol Riot, and the Chauvin Trial: Estimating Disparate News Media Stance

A Murder and Protests, the Capitol Riot, and the Chauvin Trial: Estimating Disparate News Media Stance

Sujan Dutta, Beibei Li, Daniel S. Nagin, Ashiqur R. KhudaBukhsh

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence
AI for Good. Pages 5059-5065. https://doi.org/10.24963/ijcai.2022/702

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.
Keywords:
Multidisciplinary Topics and Applications: Social Sciences
Machine Learning: Active Learning
Multidisciplinary Topics and Applications: News and Media