This project will improve scientific understanding of the consequences of police violence for population well-being and inequities in emotional and psychological health. Leveraging Twitter data and using computational text analysis, the aims of the project are to: 1) Assess how exposure to episodes of police violence in the public sphere pattern emotional well-being over time; and 2) Examine whether the emotional consequences of exposure to police violence vary by racial-ethnic, gender, age, and geography.
In 2018, 992 people were shot and killed by the police in the United States. Black men are at particularly high risk of deadly police violence relative to other groups. In addition to direct consequences of this violence, studies document a host of spill-over effects of police violence, including decreased trust in the police and increased legal cynicism. Given racial disparities in risk of police violence and a broader context of structural racism in the U.S., the collateral consequences of this violence are magnified for Black communities. Now, with the rise of portable video recorders and social media, police violence that was only observed in situ is recorded and broadcast to a global audience, made viral, and viewed repeatedly, broadening the potential reach of these spillover effects. At the same time, forms of social connectivity like Twitter offer individuals platforms for expressing their emotions in real time, offering researchers valuable insight into the effects of widely publicized events, including police violence. A growing body of research aims to identify the spillover effects of police violence, but critical gaps in scientific understanding of the role of police violence in shaping emotional and psychological outcomes—as well as population disparities in well-being—remain. The proposed project improves scientific understanding of the population impacts of police violence by using a corpus of text data from Twitter and computational text analysis to evaluate the emotional spillover effects of police violence and assess racial-ethnic, gender, age, and geographic variation in the associations between police violence and emotional well-being. The project uses longitudinal data, a quasi-natural experiment design, and two cases—the shootings of Michael Brown and Tamir
Rice—to assess whether and how police violence affects the emotional well-being of individuals, paying particular attention to the stress-related psychological processes undergirding these links as well as differential vulnerability to these events. This project will expand and shift scholarship on police violence and population disparities in well-being in three key ways. First, this study leverages big data and cutting-edge computational methods to examine the impacts of police violence on population well-being. While most research in this area relies on survey data, our use of Twitter data will allow for improved understanding of how these events shape individual and population well-being in real time. Second, we use a quasi-experimental design and difference-in-difference models to assess the links between police violence and emotional well-being, thereby improving our ability to make causal inferences. Finally, we use a variety of techniques to code sociodemographic characteristics and test for differential vulnerability of exposure to police violence by race, gender, age, and geography, providing new evidence of the role of police violence in shaping inequities in emotional and psychological well-being. Findings from this study will generate new understandings of the spillover effects of police violence, particularly as these events shape individual emotions in ways that relate to individual health and contribute to population health disparities. Results from this project will be inform applications for subsequent extramural funding focused on the links between police violence, emotions, and population disparities in psychological well-being.