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

arXiv: https://arxiv.org/abs/2109.05325 · HTML: https://mlsanity.com/paper/2109.05325/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 107 references that are in the map.
- First posted on arXiv 11 Sep 2021.

## Builds on (most cited first, 20 of 20)

- [We Can Detect Your Bias: Predicting the Political Ideology of News Articles](https://mlsanity.com/paper/2010.05338/) (2020)
- [Predicting Factuality of Reporting and Bias of News Media Sources](https://mlsanity.com/paper/1810.01765/) (2018)
- [Analyzing Polarization in Social Media: Method and Application to Tweets on 21 Mass Shootings](https://mlsanity.com/paper/1904.01596/) (2019)
- [Deep Reinforcement Learning for Mention-Ranking Coreference Models](https://mlsanity.com/paper/1609.08667/) (2016)
- [Empath: Understanding Topic Signals in Large-Scale Text](https://mlsanity.com/paper/1602.06979/) (2016)
- [Detecting Stance in Media on Global Warming](https://mlsanity.com/paper/2010.15149/) (2020)
- [Framing and Agenda-setting in Russian News: a Computational Analysis of Intricate Political Strategies](https://mlsanity.com/paper/1808.09386/) (2018)
- [Detecting East Asian Prejudice on Social Media](https://mlsanity.com/paper/2005.03909/) (2020)
- [Racism is a Virus: Anti-Asian Hate and Counterspeech in Social Media during the COVID-19 Crisis](https://mlsanity.com/paper/2005.12423/) (2020)
- [Modeling Framing in Immigration Discourse on Social Media](https://mlsanity.com/paper/2104.06443/) (2021)
- [Moral Framing and Ideological Bias of News](https://mlsanity.com/paper/2009.12979/) (2020)
- [Weakly Supervised Learning of Nuanced Frames for Analyzing Polarization in News Media](https://mlsanity.com/paper/2009.09609/) (2020)
- [Multi-Task Ordinal Regression for Jointly Predicting the Trustworthiness and the Leading Political Ideology of News Media](https://mlsanity.com/paper/1904.00542/) (2019)
- [FAKTA: An Automatic End-to-End Fact Checking System](https://mlsanity.com/paper/1906.04164/) (2019)
- [Entity-Centric Contextual Affective Analysis](https://mlsanity.com/paper/1906.01762/) (2019)
- [Identifying civilians killed by police with distantly supervised entity-event extraction](https://mlsanity.com/paper/1707.07086/) (2017)
- [Predicting the Leading Political Ideology of YouTube Channels Using Acoustic, Textual, and Metadata Information](https://mlsanity.com/paper/1910.08948/) (2019)
- [Who is Killed by Police: Introducing Supervised Attention for Hierarchical LSTMs](https://mlsanity.com/paper/1807.03409/) (2018)
- [Contextual Affective Analysis: A Case Study of People Portrayals in Online #MeToo Stories](https://mlsanity.com/paper/1904.04164/) (2019)
- [Mapping Moral Valence of Tweets Following the Killing of George Floyd](https://mlsanity.com/paper/2104.09578/) (2021)

## Built on by (most cited first, 12 of 12)

- [Can Large Language Models Transform Computational Social Science?](https://mlsanity.com/paper/2305.03514/) (2023)
- [Measuring Political Bias in Large Language Models: What Is Said and How It Is Said](https://mlsanity.com/paper/2403.18932/) (2024)
- [Multi-VALUE: A Framework for Cross-Dialectal English NLP](https://mlsanity.com/paper/2212.08011/) (2022)
- [Connecting the Dots in News Analysis: Bridging the Cross-Disciplinary Disparities in Media Bias and Framing](https://mlsanity.com/paper/2309.08069/) (2023)
- [Read Between the Lines: A Benchmark for Uncovering Political Bias in Bangla News Articles](https://mlsanity.com/paper/2510.03898/) (2025)
- [Framing Political Bias in Multilingual LLMs Across Pakistani Languages](https://mlsanity.com/paper/2506.00068/) (2025)
- [Zero-Shot Classification by Logical Reasoning on Natural Language Explanations](https://mlsanity.com/paper/2211.03252/) (2022)
- ["A Tale of Two Movements": Identifying and Comparing Perspectives in #BlackLivesMatter and #BlueLivesMatter Movements-related Tweets using Weakly Supervised Graph-based Structured Prediction](https://mlsanity.com/paper/2310.07155/) (2023)
- [Uncovering Hidden Violent Tendencies in LLMs: A Demographic Analysis via Behavioral Vignettes](https://mlsanity.com/paper/2506.20822/) (2025)
- [Fane at SemEval-2025 Task 10: Zero-Shot Entity Framing with Large Language Models](https://mlsanity.com/paper/2504.20469/) (2025)
- [Semantic-based Unsupervised Framing Analysis (SUFA): A Novel Approach for Computational Framing Analysis](https://mlsanity.com/paper/2505.15563/) (2025)
- [Quantifying Media Representation Dynamics Across 25 Years of News Reporting on Policing-related Deaths](https://mlsanity.com/paper/2606.06812/) (2026)

## Abstract (arXiv)

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.