Dataset Information

Evaluating associations between area-level Twitter-expressed negative racial sentiment, hate crimes, and residents' racial prejudice in the United States.

ABSTRACT:

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 (?: 0.11; 95% CI: 0.04, 0.18); and higher implicit racial bias (?: 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.

SUBMITTER: Nguyen TT

PROVIDER: S-EPMC7901034 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Evaluating associations between area-level Twitter-expressed negative racial sentiment, hate crimes, and residents' racial prejudice in the United States.

Nguyen Thu T TT Huang Dina D Michaels Eli K EK Glymour M Maria MM Allen Amani M AM Nguyen Quynh C QC

SSM - population health 20210206

<h4>Background</h4>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.<h4>Methods</h4>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) an ...[more]

PMID: 33665332

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Project description:Since 2015, far right parties drawing heavily on radical anti-refugee rhetoric gained electoral support in Germany while the number of political hate crimes targeting refugees rose. Both phenomena - far right electoral support and prevalence of right-wing hate crimes - have theoretically and empirically been linked with socio-structural and contextual variables. However, systematic empirical research on these links is scattered and scarce at best. We combine official statistics on political hate crimes targeting refugees in Germany and far right electoral support of the far right party "Alternative für Deutschland" (AfD) in the German national elections 2017 with socio-structural variables (proportion of foreigners and unemployment rate) and survey data collected in a representative survey (N = 1,506) in 2016. We aggregate and combine data for all German municipalities except Berlin which were the level of analysis for the current study. In path analyses, we find socio-structural variables to be unrelated with each other but significantly correlated with both criterion variables in a systematic fashion: proportion of foreigners was negatively while unemployment rate was positively linked with far right electoral support. Right-wing crime was linked positively with unemployment rate across Germany and positively with proportion of foreigners only in East Germany while proportion of foreigners was unrelated to right-wing crime in West Germany. When including survey measures into the model, they were linked with socio-structural variables in the predicted fashion - intergroup contact correlated positively with proportion of foreigners, collective deprivation correlated positively with unemployment rates, and both predicted extreme right-wing attitudes. However, their contribution to the explained variance in outcome variables above and beyond socio-structural variables was neglectable. We argue that both far right-wing electoral support and right-wing hate crime can be conceptualized as behavioral forms of political extremism shaped through socio-structural and contextual factors and discuss implications for preventing political extremism.

Dataset Information

Evaluating associations between area-level Twitter-expressed negative racial sentiment, hate crimes, and residents' racial prejudice in the United States.

Background

Methods

Results

Conclusion

Publications

Evaluating associations between area-level Twitter-expressed negative racial sentiment, hate crimes, and residents' racial prejudice in the United States.

Similar Datasets

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