This is The Geography of Hate – a cartographical collection of every geotagged tweet in the continental U.S. between June 2012 and April 2013 in which the word "chink," "gook," "nigger," "wetback," "spic," "dyke" "fag," "homo," "queer" or "cripple" was used in an explicitly negative way.
Created by the datavisualization experts at Floating Sheep, the interactive map was made in response to criticism that a previous map – which plotted the distribution of racial epithets in the wake of Obama's re-election – had arrived at specious conclusions about the relative amount of racist content emanating from Mississippi and Alabama. Via Floating Sheep:
In order to address [one such criticism] , students at Humboldt State manually read and coded the sentiment of [hundreds of thousands of tweets containing homophobic, racist, or ableist slurs] 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... All together, the students determined over 150,000 geotagged tweets with a hateful slur to be negative.
The image up top is the map of all the homophobic tweets deemed hateful. Over at the interactive map, viewers can see similar maps for racist and ableist tweets, and even parse the data to examine the geographic distributions of individual words. The results were compelling. "Even when normalized," write the researchers, "many of the slurs included in our analysis display little meaningful spatial distribution":
For example, tweets referencing ‘nigger’ are not concentrated in any single place or region in the United States; instead, quite depressingly, there are a number of pockets of concentration that demonstrate heavy usage of the word. In addition to looking at the density of hateful words, we also examined how many unique users were tweeting these words. For example in the Quad Cities (East Iowa) 31 unique Twitter users tweeted the word “nigger” in a hateful way 41 times. There are two likely reasons for higher proportion of such slurs in rural areas: demographic differences and differing social practices with regard to the use of Twitter. We will be testing the clusters of hate speech against the demographic composition of an area in a later phase of this project.