Anti-Black or African American attacks were the most common form of racist hate crime in the United States in 2023, with ***** cases. Anti-White hate crimes were the next most common form of race-based hate crime in that year, with *** incidents.

As of 2024, ** percent of surveyed Americans said that they personally worried a great deal about race relations in the United States, while ** percent said that they worried a fair amount. This is a slight decrease from the previous year, when ** percent of Americans said that they worried a great deal about race relations.

Our paper examines whether a politician charging a political candidate’s implicit racial campaign appeal as racist is an effective political strategy. According to the racial priming theory, this racialized counter-strategy should deactivate racism, thereby decreasing racially conservative whites’ support for the candidate engaged in race baiting. We propose an alternative theory in which racial liberals, and not racially conservative whites, are persuaded by this strategy. To test our theory, we focused on the 2016 presidential election. We ran an experiment varying the politician (by party and race) calling an implicit racial appeal by Donald Trump racist. We find that charging Trump’s campaign appeal as racist does not persuade racially conservative whites to decrease support for Trump. Rather, it causes racially liberal whites to evaluate Trump more unfavorably. Our results hold up when attentiveness, old-fashioned racism, and partisanship are taken into account. We also reproduce our findings in two replication studies.

In the year ending in March 2024, 31.3% of victims of racially or religiously aggravated hate crime were Asian, 30.6% were White, and 23.1% were Black.

Online, anyone’s words can easily be amplified – and on Twitter, the platform’s algorithm highlights tweets that gain attention from other users, which can exponentially reinforce a tweet’s popularity. Moreover, retweets can help spread a message well beyond the reach of its original poster. Thus, users’ interactions with posts containing or making reference to racism or sexism both illuminate the ways individuals accept, challenge, or engage with racism and sexism online, and shape how those messages spread. Using an original dataset of 59.5 million tweets, I test how particular features of messages referencing Black and Asian women predict user engagement (retweets, likes, and replies). This analysis further focuses on messages including terms that express racist or sexist content. Generally, messages including covert racist or sexist insults have a modest positive effect on all measures of user engagement (retweets, likes, and replies), which may suggest that social media environments allow individuals the time and opportunity to contend with topics that can be more difficult in-person. Additionally, variations in engagement with tweets that include references to women, Black or Asian individuals implies that users respond differently to messages involving references to and normative images of different racial, ethnic, and gendered identities. This research illuminates how specific manifestations of racialized and gendered language referencing women, Black and Asian people can not only encourage more engagement, but also share, accept, or challenge messages about marginalized identities.

ABSTRACT Brazil carries in its history centuries of slavery and racist ideologies that are reflected in its current social inequalities. Research shows that black women experience the worst access and quality of health care, which would be a consequence of institutional racism. Based on those data, a literature review was applied using the systematic review methodology with the aim to survey the Brazilian scientific production regarding institutional racism and the health of black women, as well as to analyze how the theme has been treated by researchers. It became clear that the literature on the subject remains scarce, reinforcing the need to address the theme racism in further research. Although racial inequality is confirmed in all articles analyzed, their conclusions vary among them, and some authors interpreted data solely as a consequence of economic inequality. We concluded that the debate about racism is of pivotal importance in the fight against it and that the identification of racial inequality with economic condition is a consequence of the racial democracy myth that contributes to the institutional racism perpetuation. Raising awareness about racism is needed among professionals so that it becomes essential to consider the category ‘race’ for equal health.

Racist incidents for all police force areas

Source: Ministry of Justice (MoJ)

Publisher: Ministry of Justice

Geographies: Police Force Area

Geographic coverage: England and Wales

Time coverage: 1999/2000 to 2006/07

Type of data: Administrative data

According to the 2021 Census, London was the most ethnically diverse region in England and Wales – 63.2% of residents identified with an ethnic minority group.

CalEnviroScreen scores represent a combined measure of pollution and the potential vulnerability of a population to the effects of pollution. Like the previous versions, CalEnviroScreen 4.0 does not include indicators of race/ethnicity or age. However, the distribution of the CalEnviroScreen 4.0 cumulative impact scores by race or ethnicity is important. This information can be used to better understand issues related to environmental justice and racial equity in California. CalEPAs racial equity team has released a StoryMap using CalEnviroScreen 3.0 data that examines the connection between racist land use practices of the 1930s and the persistence of environmental injustice. The CalEPA StoryMap, along with this analysis, are examples of information that can be used to better understand issues related to environmental justice and racial equity in California.

The statistic shows the share of U.S. population, by race and Hispanic origin, in 2016 and a projection for 2060. As of 2016, about 17.79 percent of the U.S. population was of Hispanic origin. Race and ethnicity in the U.S. For decades, America was a melting pot of the racial and ethnical diversity of its population. The number of people of different ethnic groups in the United States has been growing steadily over the last decade, as has the population in total. For example, 35.81 million Black or African Americans were counted in the U.S. in 2000, while 43.5 million Black or African Americans were counted in 2017.

The median annual family income in the United States in 2017 earned by Black families was about 50,870 U.S. dollars, while the average family income earned by the Asian population was about 92,784 U.S. dollars. This is more than 15,000 U.S. dollars higher than the U.S. average family income, which was 75,938 U.S. dollars.

The unemployment rate varies by ethnicity as well. In 2018, about 6.5 percent of the Black or African American population in the United States were unemployed. In contrast to that, only three percent of the population with Asian origin was unemployed.

When debates about Islam acquire importance in the public sphere, does the far right adhere to traditional racist arguments, risking marginalization, or does it conform to mainstream values to attain legitimacy in the political system? Focusing on the aftermath of the 2015 terrorist attacks in France, I explore the framing of Islam, discussing how the far right’s nativist arguments were reformulated to engage with available discursive opportunities and dominant conceptions of the national identity. By looking at actors in the protest and the electoral arenas, I examine the interplay between the choice of anti-Islam frames and baseline national values. I offer a novel mixed-method approach to study political discourses, combining social network analysis of the links between seventy-seven far-right websites with a qualitative frame analysis of online material. It also includes measures of online visibility of these websites to assess their audiences. The results confirm that anti-Islam frames are couched along a spectrum of discursive opportunity, where actors can either opt to justify opposition to Islam based on interpretations of core national values (culture and religion) or mobilize on strictly oppositional values (biological racism). The framing strategy providing most online visibility is based on neo-racist arguments. While this strategy allows distortion of baseline national values of secularity and republicanism, without breaching the social contract, it is also a danger for organizations that made “opposition to the system” their trademark. While the results owe much to the French context, the conclusions draw broader implications as to the far right going mainstream.

According to a survey conducted in 2024, ** percent of Americans said that they thought that the relations between racial groups in the United States are getting worse in the last five years, while ** percent said that the relations between racial groups have stayed the same.

Observers have long noted Brazil’s distinctive racial politics: the coexistence of relatively integrated race relations and a national ideology of “racial democracy” with deep social inequalities along color lines. Those defending a vision of a non-racist Brazil attribute such inequalities to mechanisms perpetuating class distinctions. We examine how members of disadvantaged groups perceive their disadvantage and determinants of self-reports of discriminatory experiences, using 2010 AmericasBarometer data. About a third of respondents reports experiencing discrimination. Consistent with Brazilian national myths, respondents are much more likely to report discrimination due to their class than to their race. Nonetheless, the respondent’s skin color, as coded by the interviewer, is a strong determinant of reporting class as well as race and gender discrimination. Race is more strongly associated with perceived “class” discrimination than are household wealth, education, or region of residence; female gender intensifies the association between color and discrimination.

The number of reported racist incidents to police forces in England and Wales (excluding British Transport Police). A ‘racist incident’ is any incident, including any crime, which is perceived by the victim or any other person to be motivated by a hostility or prejudice based on a person’s race or perceived race.

In 2023, there were ***** victims of anti-Black or African American hate crimes in the United States, making it the racially motivated hate crime with the most victims in that year. The second most common racially motivated hate crime, anti-Hispanic or Latino crimes, had ***** victims in that year.

PLEASE NOTE: This is an index of a historical collection that contains words and phrases that may be offensive or harmful to individuals investigating these records. In order to preserve the objectivity and historical accuracy of the index, State Archives staff took what would today be considered archaic and offensive descriptions concerning race, ethnicity, and gender directly from the original court papers. For more information on appropriate description, please consult the Diversity Style Guide and Archives for Black Lives in Philadelphia: Anti-Racist Description Resources. The Litchfield County Court African Americans and Native Americans Collection is an artificial collection consisting of photocopies of cases involving persons of African descent and indigenous people from the Files and Papers by Subject series of Litchfield County Court records. This collection was created in order to highlight the lives and experiences of underrepresented groups in early America, and make them more easily accessible to researchers. Collection Overview The collection consists of records of 188 court cases involving either African Americans or Native Americans. A careful search of the Files for the Litchfield County Court discovered 165 on African Americans and 23 on Native Americans, about one third of the total that was found in Files for the New London County Court for the period up to the American Revolution. A couple of reasons exist for this vast difference in numbers. First, Litchfield County was organized much later than New London, one of Connecticut's four original counties. New London was the home of four of seven recognized tribes, was a trading center, and an area of much greater wealth. Second, minority population in the New London County region has been tracked and tabulated by Barbara Brown and James Rose in Black Roots of Southeastern Connecticut.1 Although this valuable work does not include all of Negro or Indian background, it provides a wonderful starting point and it has proven to be of some assistance in tracking down minorities in Litchfield County. In most instances, however, identification is based upon language in the documents and knowledge of surnames or first names.2 Neither surname nor first name provides an invariably reliable guide so it is possible that some minorities have been missed and some persons included that are erroneous. In thirteen of 188 court cases, the person of African or Native American background cannot be identified even by first name. He or she is noted as "my Negro," a slave girl, or an Indian. In twenty-three lawsuits, a person with a first name is identified as a Negro, as an Indian in two other cases, and Mulatto in one. In the remaining 151 cases, a least one African American or Native American is identified by complete name.3 Thirteen surnames recur in three or more cases.4 A total of seventy surnames, some with more than one spelling, are represented in the records. The Jacklin surname appears most frequently represented in the records. Seven different Jacklins are found in eighteen cases, two for debt and the remaining sixteen for more serious crimes like assault, breach of peace, keeping a bawdy house, and trespass.5 Ten cases concern Cuff Kingsbury of Canaan between 1808 and 1812, all involving debts against Kingsbury and the attempts of plaintiffs to secure writs of execution against him. Cyrus, Daniel, Ebenezer, Jude, Luke, Martin, Nathaniel, Pomp, Titus, and William Freeman are found in nine cases, some for debt, others for theft, and one concerning a petition to appoint a guardian for aged and incompe

A hand-labeled training (50,000 tweets labeled twice) and evaluation set (10,000 tweets labeled twice) for hate speech on Slovenian Twitter. The data files contain tweet IDs, hate speech type, hate speech target, and annotator ID. For obtaining the full text of the dataset, please contact the first author.

Hate speech type:

1. Appropriate - has no target

2. Inappropriate (contains terms that are obscene, vulgar; but the text is not directed at any person specifically) - has no target

3. Offensive (including offensive generalization, contempt, dehumanization, indirect offensive remarks)

4. Violent (author threatens, indulges, desires, or calls for physical violence against a target; it also includes calling for, denying, or glorifying war crimes and crimes against humanity)

Hate speech target:

1. Racism (intolerance based on nationality, ethnicity, language, towards foreigners; and based on race, skin color)

2. Migrants (intolerance of refugees or migrants, offensive generalization, call for their exclusion, restriction of rights, non-acceptance, denial of assistance…)

3. Islamophobia (intolerance towards Muslims)

4. Antisemitism (intolerance of Jews; also includes conspiracy theories, Holocaust denial or glorification, offensive stereotypes…)

5. Religion (other than above)

6. Homophobia (intolerance based on sexual orientation and / or identity, calls for restrictions on the rights of LGBTQ persons

7. Sexism (offensive gender-based generalization, misogynistic insults, unjustified gender discrimination)

8. Ideology (intolerance based on political affiliation, political belief, ideology… e.g. “communists”, “leftists”, “home defenders”, “socialists”, “activists for…”)

9. Media (journalists and media, also includes allegations of unprofessional reporting, false news, bias)

10. Politics (intolerance towards individual politicians, authorities, system, political parties)

11. Individual (intolerance toward any other individual due to individual characteristics; like commentator, neighbor, acquaintance )

12. Other (intolerance towards members of other groups due to belonging to this group; write in the blank column on the right which group it is)

Training dataset

The training set is sampled from data collected between December 2017 and February 2020. The sampling was intentionally biased to contain as much hate speech as possible. A simple model was used to flag potential hate speech content and additionally, filtering by users and by tweet length (number of characters) was applied. 50,000 tweets were selected for annotation.

Evaluation dataset

The evaluation set is sampled from data collected between February 2020 and August 2020. Contrary to the training set, the evaluation set is an unbiased random sample. Since the evaluation set is from a later period compared to the training set, the possibility of data linkage is minimized. Furthermore, the estimates of model performance made on the evaluation set are realistic, or even pessimistic, since the evaluation set is characterized by a new topic: Covid-19. 10,000 tweets were selected for the evaluation set.

Annotation results

Each tweet was annotated twice: In 90% of the cases by two different annotators and in 10% of the cases by the same annotator. Special attention was devoted to evening out the overlap between annotators to get agreement estimates on equally sized sets.

Ten annotators were engaged for our annotation campaign. They were given annotation guidelines, a training session, and a test on a small set to evaluate their understanding of the task and their commitment before starting the annotation procedure. Annotator agreement in terms of Krippendorff Alpha is around 0.6. Annotation agreement scores are detailed in the accompanying report files for each dataset separately.

The annotation process lasted four months, and it required about 1,200 person-hours for the ten annotators to complete the task.

PLEASE NOTE: This is an index of a historical collection that contains words and phrases that may be offensive or harmful to individuals investigating these records. In order to preserve the objectivity and historical accuracy of the index, State Archives staff took what would today be considered archaic and offensive descriptions concerning race, ethnicity, and gender directly from the original court papers. For more information on appropriate description, please consult the Diversity Style Guide and Archives for Black Lives in Philadelphia: Anti-Racist Description Resources.

This collection contains over a thousand records of cases involving persons of African descent, both enslaved and free. It was created in order to highlight the lives and experiences of underrepresented groups in early America, and make them more easily accessible to researchers.

If a record of interest is found, and a reproduction of the original record is desired, you may submit a request via e-mail or by contacting the History & Genealogy Unit of the Connecticut State Library at (860) 757-6580. Please include the names of the parties, if known, as well as the box and folder numbers.

Reproduction formats and fees available, are as follows:

Photocopy: black & white copy, 8 1/2 X 11″ or 11 X 14″ sized paper, 25 cents; 11 X 17″, 50 cents per photocopied page, plus a $3.00 handling fee and first class postage charges. Photocopy: color copy 8 1/2 X 11″ or 11 X 14″ sized paper, $1.00 per photocopied page, 11 X 17″, $1.25 per photocopied page plus a $3.00 handling fee and first class postage charges.

Digital images (low or high resolution): PDF, JEG, TIFF, or DNG images, 25 cents per image, plus a $3.00 handling fee. Digital file may be delivered via internet for no additional cost. Pre-payment is not needed as a bill will accompany the finished product, either in the mail with photocopies or with the digital images.