Note: for questions about the methodology/approach of this post, see the FAQ (added 16:20 EST 11/9/2012).

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in an interactive version) in which the location of individual tweets (indicated by red dots)

[4]

are overlaid on color coded states. Yellow shading indicates states that have a relatively lower amount of post-election hate tweets (compared to their overall tweeting patterns) and all states shaded in green have a higher amount. T

he darker the green color the higher the location quotient measure for hate tweets.

Map of the Location Quotients for Post Election Racist Tweets



Click here to access an interactive version of the map at GeoCommons





Mississippi and Alabama have the highest LQ measures with scores of 7.4 and 8.1, respectively.

Other southern states (Georgia, Louisiana, Tennessee) surrounding these two core states also have very high LQ scores and form a fairly distinctive cluster in the southeast.

The prevalence of post-election racist tweets is not strictly a southern phenomenon as North Dakota (3.5), Utah (3.5) and Missouri (3) have very high LQs. Other states such as West Virginia, Oregon and Minnesota don't score as high but have a relatively higher number of hate tweets than their overall twitter usage would suggest.

The Northeast and West coast (with the exception of Oregon) have a relatively lower number of hate tweets.

States shaded in grey had no geocoded hate tweets within our database. Many of these states (Montana, Idaho, Wyoming and South Dakota) have relatively low levels of Twitter use as well. Rhode Island has much higher numbers of geocoded tweets but had no hate tweets that we could identify.



State LQ of Racist Tweets Notes Alabama 8.1 Mississippi 7.4 Georgia 3.6 North Dakota 3.5 Utah 3.5 Louisiana 3.3 Tennessee 3.1 Missouri 3.0 West Virginia 2.8 Minnesota 2.7 Kansas 2.4 Kentucky 1.9 Arkansas 1.9 Wisconsin 1.9 Colorado 1.9 New Mexico 1.6 Maryland 1.6 Illinois 1.5 North Carolina 1.5 Virginia 1.5 Oregon 1.5 District of Columbia 1.5 Ohio 1.4 South Carolina 1.4 Texas 1.3 Florida 1.3 Delaware 1.3 Nebraska 1.1 Washington 1.0 Maine 0.9 New Hampshire 0.8 Pennsylvania 0.7 Michigan 0.6 Massachusetts 0.5 New Jersey 0.5 California 0.5 Oklahoma 0.5 Connecticut 0.5 Nevada 0.5 Iowa 0.4 Indiana 0.3 New York 0.3 Arizona 0.2 Alaska - see note 1 Idaho - see note 1 South Dakota - see note 1 Wyoming - see note 1 Montana - see note 1 Hawaii - see note 1 Vermont - see note 1 Rhode Island - see note 2



Note 1: no racist tweets, SMALL number of total geocoded tweets

Note 2: no racist tweets, LARGE number of total geocoded tweets Note 1: no racist tweets, SMALL number of total geocoded tweets





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[1] Using the examples of tweets chronicled by Jezebel blog post we collected tweets that contained the text "monkey" or "nigger" AND also contain the text "Obama" OR "reelected" OR "won". A quick, and very unsettling, examination of the search results revealed that this indeed was a good match for our target of election-related hate speech. We end up with a total of 395 of some of the nastiest tweets you might possibly imagine. And given that we're talking about the Internet, that is really saying something.

[2] To be precise, we took a 0.05% sample of all geocoded tweets in November 2012 aggregated to the state level.



[3] The formula for this location quotient is



(# of Hate Tweets in State / # of Hate Tweets in USA) ------------------------------------------------------------



[4] We should also note that the precision of the individual tweet locations is variable. Often the specific location shown in a map is the centroid of an area that is several tens or hundreds of meters across so while the tweet came from nearby the point location shown it did not necessarily come from that exact spot on the map. (# of ALL Tweets in State / # of ALL Tweets in USA) [3] The formula for this location quotient is

During the day after the 2012 presidential election we took note of a spike in hate speech on Twitter referring to President Obama's re-election, as chronicled by Jezebel (thanks to Chris Van Dyke for bringing this our attention). It is a useful reminder that technology reflects the society in which it is based, both the good and the bad. Information space is not divorced from everyday life and racism extends into the geoweb and helps shapes its contours; and in turn, data from the geoweb can be used to reflect the geographies of racist practice back onto the places from which they emerged.Using DOLLY we collected all the geocoded tweets from the last week (beginning November 1) with racist terms that also reference the election in order to understand how these everyday acts of explicit racism are spatially distributed. Given the nature of these search terms, we've buried the details at the bottom of this post in a footnote [1].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 [2]. 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.[3] 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.So, are these tweets relatively evenly distributed? Or do some states have higher specializations in racist tweets? The answer is shown in the map below (also available here A couple of findings from this analysisKeep 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.But lest anyone elsewhere become too complacent, the unfortunate fact is that most states are not immune from this kind of activity. Racist behavior, particularly directed at African Americans in the U.S., is all too easy to find both offline and in information space.--------------------- State Level Data ---------------------The table below outlines the values for the location quotients for post-election hate tweets.