A Murder and Protests, the Capitol Riot, and the Chauvin Trial: Estimating Disparate News Media Stance
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.
Cite
Text
Dutta et al. "A Murder and Protests, the Capitol Riot, and the Chauvin Trial: Estimating Disparate News Media Stance." International Joint Conference on Artificial Intelligence, 2022. doi:10.24963/IJCAI.2022/702Markdown
[Dutta et al. "A Murder and Protests, the Capitol Riot, and the Chauvin Trial: Estimating Disparate News Media Stance." International Joint Conference on Artificial Intelligence, 2022.](https://mlanthology.org/ijcai/2022/dutta2022ijcai-murder/) doi:10.24963/IJCAI.2022/702BibTeX
@inproceedings{dutta2022ijcai-murder,
title = {{A Murder and Protests, the Capitol Riot, and the Chauvin Trial: Estimating Disparate News Media Stance}},
author = {Dutta, Sujan and Li, Beibei and Nagin, Daniel S. and KhudaBukhsh, Ashiqur R.},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2022},
pages = {5059-5065},
doi = {10.24963/IJCAI.2022/702},
url = {https://mlanthology.org/ijcai/2022/dutta2022ijcai-murder/}
}