UC Berkeley

BACKGROUND: The objective of the current study is to investigate whether an area-level measure of racial sentiment derived from Twitter data is associated with state-level hate crimes and existing measures of racial prejudice at the individual-level. METHODS: We collected 30,977,757 tweets from June 2015-July 2018 containing at least one keyword pertaining to specific groups (Asians, Arabs, Blacks, Latinos, Whites). We characterized sentiment of each tweet (negative vs all other) and averaged at the state-level. These racial sentiment measures were merged with other measures based on: hate crime data from the FBI Uniform Crime Reporting Program; implicit and explicit racial bias indicators from Project Implicit; and racial attitudes questions from General Social Survey (GSS). RESULTS: Living in a state with 10% higher negative sentiment in tweets referencing Blacks was associated with 0.57 times the odds of endorsing a GSS question that Black-White disparities in jobs, income, and housing were due to discrimination (95% CI: 0.40, 0.83); 1.64 times the odds of endorsing the belief that disparities were due to lack to will (95% CI: 0.95, 2.84); higher explicit racial bias (β: 0.11; 95% CI: 0.04, 0.18); and higher implicit racial bias (β: 0.09; 95% CI: 0.04, 0.14). Twitter-expressed racial sentiment was not statistically-significantly associated with incidence of state-level hate crimes against Blacks (IRR: 0.99; 95% CI: 0.52, 1.90), but this analysis was likely underpowered due to rarity of reported hate crimes. CONCLUSION: Leveraging timely data sources for measuring area-level racial sentiment can provide new opportunities for investigating the impact of racial bias on society and health.