Let’s face it — an African-American man is President. Well, a bi-racial man who certainly shows his genetic heritage. Something else we have to face, unfortunately, is that in America, our many-race culture has resulted in racist hatred that is all too common. With Obama winning reelection, the racists are certainly going to make themselves heard, and one enterprising company went out of their way to go ahead and try to figure out where they were coming from, using Twitter data. Curious? Here’s the map:
You can find the entirety of the study here, at Floating Sheep (the organization that conducted the data mining). It should be noted that this is not a scientific study; however, objectivity was the goal and objectivity was achieved. Out of the relatively small sample size taken (0.05% of Tweets and the racially inspired ones isolated from there for a total of 395. Several comments we have seen on the study or articles regarding it have revealed that people think that, for instance, African-Americans using the term “nigger” as slang would count toward the total (let’s be serious, though, that’s a desperate attempt to have your area look less racist; the entire word is not used and we all know that), but the researchers explicitly state that the 395 Tweets used were read.
Floating Sheep states,
Keep in mind we are measuring tweets rather than users and so one individual could be responsible for many tweets and in some cases (most notably in North Dakota, Utah and Minnesota) the number of hate tweets is small and the high LQ is driven by the relatively low number of overall tweets. Nonetheless, these findings support the idea that there are some fairly strong clustering of hate tweets centered in southeastern U.S. which has a much higher rate than the national average.
They also explain their methodology:
Given our interest in the geography of information we wanted to see how this type of hate speech overlaid on physical space. To do this we aggregated the 395 hate tweets to the state level and then normalized them by comparing them to the total number of geocoded tweets coming out of that state in the same time period . We used a location quotient inspired measure (LQ) that indicates each state’s share of election hate speech tweet relative to its total number of tweets. A score of 1.0 indicates that a state has relatively the same number of hate speech tweets as its total number of tweets. Scores above 1.0 indicate that hate speech is more prevalent than all tweets, suggesting that the state’s “twitterspace” contains more racists post-election tweets than the norm.
A full explanation can be found at the link above, or here. Their FAQ page on the study is found here, and answers questions regarding the sample size (395 isn’t the sample size, it’s the number of relevant Tweets from the sample size), multiple Tweets, etc.