To Protect and To Serve? Analyzing Entity-Centric Framing of Police Violence

Caleb Ziems, Diyi Yang


Abstract
Framing has significant but subtle effects on public opinion and policy. We propose an NLP framework to measure entity-centric frames. We use it to understand media coverage on police violence in the United States in a new Police Violence Frames Corpus of 82k news articles spanning 7k police killings. Our work uncovers more than a dozen framing devices and reveals significant differences in the way liberal and conservative news sources frame both the issue of police violence and the entities involved. Conservative sources emphasize when the victim is armed or attacking an officer and are more likely to mention the victim’s criminal record. Liberal sources focus more on the underlying systemic injustice, highlighting the victim’s race and that they were unarmed. We discover temporary spikes in these injustice frames near high-profile shooting events, and finally, we show protest volume correlates with and precedes media framing decisions.
Anthology ID:
2021.findings-emnlp.82
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2021
Month:
November
Year:
2021
Address:
Punta Cana, Dominican Republic
Editors:
Marie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-tau Yih
Venue:
Findings
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
957–976
Language:
URL:
https://aclanthology.org/2021.findings-emnlp.82
DOI:
10.18653/v1/2021.findings-emnlp.82
Bibkey:
Cite (ACL):
Caleb Ziems and Diyi Yang. 2021. To Protect and To Serve? Analyzing Entity-Centric Framing of Police Violence. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 957–976, Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
To Protect and To Serve? Analyzing Entity-Centric Framing of Police Violence (Ziems & Yang, Findings 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.findings-emnlp.82.pdf
Video:
 https://aclanthology.org/2021.findings-emnlp.82.mp4