The racist map of America: Tweets analyzed for offensive keywords reveal the most bigoted parts of the US and which people are the most hated

The project Geography of Hate was created by cartography students at Humboldt State University

Students analyzed 150,000 tweets containing hate words sent between June 2012-April 2013

Researchers looked at usage of 10 slurs in three categories: racist, homophobic and disability

Use of offensive term n***** was not concentration in any single region, but had pockets of concentration in Iowa and Indiana

Racism, homophobia and general intolerance are not unique to any particular region of the US - that is the conclusion that California college students have reached after mapping out hate speech based on Twitter posts.



Undergraduate students at Humboldt State University analyzed 150,000 geocoded tweets sent out between June 2012 and April 2013 containing 10 pre-selected hate words in three categories: Racism, homophobia and disability.



After processing the data aggregated by the DOLLY Project , the team comprised of three students in Dr Monica Stephens’ advance cartography class produced an interactive map as part of The Geography of Hate project.



Mapping bigotry: Undergraduate students at Humboldt State University analyzed 150,000 geocoded tweets containing 10 pre-selected hate words in three categories: Racism, homophobia and disability

Disturbing findings: Researchers discovered 41,306 tweets containing the word 'n*****,' which were not concentrated in any single region of the US

The avoid the pitfall of an algorithm automatically classifying a tweet as negative if it contains a 'hate word,' the organizers of the project relied on students to read the entirety of the message for context before deciding if is tweet is in fact hateful.



Only words unequivocally deemed as ‘hate speech’ were used in the creation of the map. That way, a phrase like ‘dykes on bikes,’ for example, was left out of the data used in the project because it referenced a gay pride event in San Francisco.



To produce the map, all tweets containing each 'hate word' were aggregated to the county level and normalized by the total Twitter traffic in each county.

Human touch: Since an algorithm would automatically classify a tweet as negative if it contains a 'hate word,' the organizers of the project had the students to read the entirety of the message before deciding if is hateful

America's true colors: Where there is a larger proportion of negative tweets referencing a particular 'hate word' the region appears red; where the proportion is moderate, the area is shaded a pale blue

Where there is a larger proportion of negative tweets referencing a particular 'hate word' the region appears red on the map; where the proportion is moderate, the word was used less and appears a pale blue on the map.



Areas without shading indicate places that have a lower proportion of negative tweets relative to the national average.

Researchers discovered 41,306 tweets containing the word ‘n*****’, 95,123 referenced ‘homo’, among other terms.



Tweets that included the slur ‘n*****’ used for African-Americans were not concentrated in any single region in the US; instead, there are a number of pockets of concentration, including East Iowa, where 31 users sent out 41 tweets referencing the word, and Fountain, Indiana, where there were 22 tweets containing the slur.



Pockets of hatred: Most of the tweets containing the word 'wetback ' - an offensive term of illegal Mexican immigrants - came from several parts of Texas

Perhaps the most interesting concentration comes for references to ‘wetback’ - a derogatory term used for illegal Mexican immigrants. Most tweets containing the offensive term came from several parts of Texas, which surprisingly are not even close to the Mexican-American border.



Under the category of racism, besides 'n***** and ‘wetback’ students also looked at the usage of such slurs as 'chink' and 'gook' refering to Chinese and Korenas, respectively, and 'spick,' which is an offensive term for Hispanics.