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Visualizing

Mapping The Most Hate-Filled Places In America

The maps from Geography of Hate look at where across the country people are most likely to be tweeting something deeply hateful.

  • <p>The Geography of Hate project mapped 150,000 tweets that used racist or homophobic terms.</p>

<p>This map shows tweets with the word "fag."</p>
  • <p>The maps were put together by Monica Stephens an assistant professor at Humboldt State.</p>

<p>This map shows tweets with the word "dyke."</p>
  • <p>To make sense of the 150,000 tweets, three of Stephens’ students (Amelia Egle, Miles Ross and Matthew Eiben) first had to find only the negative ones.</p>

<p>This map shows tweets with the word "homo."</p>
  • <p>They didn’t want phrases celebrating “dykes on bikes #SFPride.”</p>

<p>This map shows tweets with the word "queer."</p>
  • <p>Stephens says the job took about 150 hours in total, but was worth it because algorithmic analysis couldn’t have achieved the same thing.</p>

<p>This map shows tweets with the word "nigger."</p>
  • <p>This map shows tweets with the word "chink."</p>
  • <p>This map shows tweets with the word "gook."</p>
  • <p>This map shows tweets with the word "spick."</p>
  • <p>This map shows tweets with the word "wetback."</p>
  • <p>This map shows tweets with the word "cripple."</p>
  • 01 /10

    The Geography of Hate project mapped 150,000 tweets that used racist or homophobic terms.

    This map shows tweets with the word "fag."

  • 02 /10

    The maps were put together by Monica Stephens an assistant professor at Humboldt State.

    This map shows tweets with the word "dyke."

  • 03 /10

    To make sense of the 150,000 tweets, three of Stephens’ students (Amelia Egle, Miles Ross and Matthew Eiben) first had to find only the negative ones.

    This map shows tweets with the word "homo."

  • 04 /10

    They didn’t want phrases celebrating “dykes on bikes #SFPride.”

    This map shows tweets with the word "queer."

  • 05 /10

    Stephens says the job took about 150 hours in total, but was worth it because algorithmic analysis couldn’t have achieved the same thing.

    This map shows tweets with the word "nigger."

  • 06 /10

    This map shows tweets with the word "chink."

  • 07 /10

    This map shows tweets with the word "gook."

  • 08 /10

    This map shows tweets with the word "spick."

  • 09 /10

    This map shows tweets with the word "wetback."

  • 10 /10

    This map shows tweets with the word "cripple."

If you’ve wondered where the truly hate-filled people in America live, take a look at these maps. They’re based on an analysis of 150,000 geo-located tweets from June last year to this April, and show where people are using Twitter to voice off homophobic and racist slurs.

It’s called the Geography of Hate. You can see, for example, that parts of Indiana and Iowa have the greatest numbers of people using the word "nigger" negatively, and that there are slightly more people using the words "homo" or "fag" in Omaha and Green Bay (though many places use them).

A map showing homophobic tweets with the word "dyke" (but not "dykes on bikes").

You can search for "dyke," "fag," "homo," and "queer" in the homophobic category, "chink," "gook," "nigger," "wetback," and "spick" in the racist column, and "cripple" under disability. The maps show relative use: where people tweeted the offending terms most often. The worst are in red, the moderately bad are in blue, with the least bad are unshaded.

The maps were put together by Monica Stephens, an assistant professor at Humboldt State. "As all the data is normalized based on how much content there is on
Twitter, the map primarily shows areas where the proportion of content is above average," she says. "As you’ll note from the maps, racist and homophobic sentiment exists around the country and not just in red states."

The map showing racist tweets with the word "nigger."

Though the maps make use of new technology like Twitter and Google’s Map API, they show some limitations, too. To make sense of the 150,000 tweets, three of Stephens’ students (Amelia Egle, Miles Ross and Matthew Eiben) first had to find only the negative ones (they didn’t want phrases celebrating "dykes on bikes #SFPride"). Stephens says the job took about 150 hours in total, but was worth it because algorithmic analysis couldn’t have achieved the same thing.

"Computers are poor judges of sarcasm, and Twitter is often used in sarcastic ways, or used to quote somebody else. Most sentiment analyzers would put all words that include these terms in the negative category.

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