To Protect and To Serve? Analyzing Entity-Centric Framing of Police Violence
This work addresses the subtle effects of media framing on public opinion and policy regarding police violence, providing a new tool for analyzing media bias and its societal impact.
The authors tackled the problem of measuring media framing of police violence by developing an NLP framework to analyze entity-centric frames in news articles, revealing significant differences between liberal and conservative sources in how they frame victims and issues, with conservative sources emphasizing victim actions and criminal records while liberal sources focus on systemic injustice and race.
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.