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.

BACKGROUND: Research has demonstrated the negative impact of racism on health, yet the measurement of racial sentiment remains challenging. This article provides practical guidance on using social media data for measuring public sentiment. METHODS: We describe the main steps of such research, including data collection, data cleaning, binary sentiment analysis, and visualization of findings. We randomly sampled 55,844,310 publicly available tweets from 1 January 2011 to 31 December 2021 using Twitters Application Programming Interface. We restricted analyses to US tweets in English using one or more 90 race-related keywords. We used a Support Vector Machine, a supervised machine learning model, for sentiment analysis. RESULTS: The proportion of tweets referencing racially minoritized groups that were negative increased at the county, state, and national levels, with a 16.5% increase at the national level from 2011 to 2021. Tweets referencing Black and Middle Eastern people consistently had the highest proportion of negative sentiment compared with all other groups. Stratifying temporal trends by racial and ethnic groups revealed unique patterns reflecting historical events specific to each group, such as the killing of George Floyd regarding sentiment of posts referencing Black people, discussions of the border crisis near the 2018 midterm elections and anti-Latinx sentiment, and the emergence of COVID-19 and anti-Asian sentiment. CONCLUSIONS: This study demonstrates the utility of social media data as a quantitative means to measure racial sentiment over time and place. This approach can be extended to a range of public health topics to investigate how changes in social and cultural norms impact behaviors and policy.A supplemental digital video is available at http://links.lww.com/EDE/C91.

This study examined whether killings of George Floyd, Ahmaud Arbery, and Breonna Taylor by current or former law enforcement officers in 2020 were followed by shifts in public sentiment toward Black people. Methods: Google searches for the names "Ahmaud Arbery," "Breonna Taylor," and "George Floyd" were obtained from the Google Health Application Programming Interface (API). Using the Twitter API, we collected a 1% random sample of publicly available U.S. race-related tweets from November 2019-September 2020 (N = 3,380,616). Sentiment analysis was performed using Support Vector Machines, a supervised machine learning model. A qualitative content analysis was conducted on a random sample of 3,000 tweets to understand themes in discussions of race and racism and inform interpretation of the quantitative trends. Results: The highest rate of Google searches for any of the three names was for George Floyd during the week of May 31 to June 6, the week after his murder. The percent of tweets referencing Black people that were negative decreased by 32% (from 49.33% in November 4-9 to 33.66% in June 1-7) (p < 0.001), but this decline was temporary, lasting just a few weeks. Themes that emerged during the content analysis included discussion of race or racism in positive (14%) or negative (38%) tones, call for action related to racism (18%), and counter movement/arguments against racism-related changes (6%). Conclusion: Although there was a sharp decline in negative Black sentiment and increased public awareness of structural racism and desire for long-lasting social change, these shifts were transitory and returned to baseline after several weeks. Findings suggest that negative attitudes towards Black people remain deeply entrenched.

Background: Anecdotal reports suggest a rise in anti-Asian racial attitudes and discrimination in response to COVID-19. Racism can have significant social, economic, and health impacts, but there has been little systematic investigation of increases in anti-Asian prejudice. Methods: We utilized Twitter's Streaming Application Programming Interface (API) to collect 3,377,295 U.S. race-related tweets from November 2019-June 2020. Sentiment analysis was performed using support vector machine (SVM), a supervised machine learning model. Accuracy for identifying negative sentiments, comparing the machine learning model to manually labeled tweets was 91%. We investigated changes in racial sentiment before and following the emergence of COVID-19. Results: The proportion of negative tweets referencing Asians increased by 68.4% (from 9.79% in November to 16.49% in March). In contrast, the proportion of negative tweets referencing other racial/ethnic minorities (Blacks and Latinx) remained relatively stable during this time period, declining less than 1% for tweets referencing Blacks and increasing by 2% for tweets referencing Latinx. Common themes that emerged during the content analysis of a random subsample of 3300 tweets included: racism and blame (20%), anti-racism (20%), and daily life impact (27%). Conclusion: Social media data can be used to provide timely information to investigate shifts in area-level racial sentiment.