A group of students analyzed tweets from across the country for hateful terms. Rather than use algorithms, they read each tweet and then classified them by geographic location. The result is stunning.
May 14, 2013

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Where hate is concentrated

If you've spent any time on Twitter, you've inevitably bumped up against someone who is hateful. If you tweet about politics on Twitter, racist and homophobic tweets are as common as falling leaves on an autumn day. They come from many different sources, as you've seen just from our Stupid Right-Wing Tweets feature.

Geographically, though, they emanate from a concentrated area of the South and Midwest, as shown on the map above, as analyzed by FloatingSheep.org:

The prominence of debates around online bullying and the censorship of hate speech prompted us to examine how social media has become an important conduit for hate speech, and how particular terminology used to degrade a given minority group is expressed geographically. As we’ve documented in a variety of cases, the virtual spaces of social media are intensely tied to particular socio-spatial contexts in the offline world, and as this work shows, the geography of online hate speech is no different.

Rather than focusing just on hate directed towards a single individual at a single point in time, we wanted to analyze a broader swath of discriminatory speech in social media, including the usage of racist, homophobic and ableist slurs.

Using DOLLY to search for all geotagged tweets in North America between June 2012 and April 2013, we discovered 41,306 tweets containing the word ‘nigger’, 95,123 referenced ‘homo’, among other terms. In order to address one of the earlier criticisms of our map of racism directed at Obama, students at Humboldt State manually read and coded the sentiment of each tweet to determine if the given word was used in a positive, negative or neutral manner. This allowed us to avoid using any algorithmic sentiment analysis or natural language processing, as many algorithms would have simply classified a tweet as ‘negative’ when the word was used in a neutral or positive way. For example the phrase ‘dyke’, while often negative when referring to an individual person, was also used in positive ways (e.g. “dykes on bikes #SFPride”). The students were able to discern which were negative, neutral, or positive. Only those tweets used in an explicitly negative way are included in the map.

That last paragraph describes how important this study really is. By actually analyzing the context and meaning contained in the individual tweets, students were able to cull out those tweets which may have used their key terms but not in an intended negative manner.

The geographical distribution isn't particularly surprising, but it's still a stark reminder that nearly half the country is still deeply invested in maintaining the hate. One thing is for sure: We do not live in a post-racial society, nor does it seem as though that will ever be a possibility.

One other thing. If you go to the interactive map and look at where the term "wetback" is used most often, here's the result:

Concentration for the term "wetback"

Congressman Young's use of the term must have fallen into the standard deviation, but a look at that map certainly explains why Texas would rather secede than approve immigration reform.

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