Mapping Racist Tweets in Response to President Obama's Re-election

Note: for questions about the methodology/approach of this post, see the FAQ (added 16:20 EST 11/9/2012).
Note: as of 11:00 EST 11/10/2012, we have disabled commenting on this post.
Note: at 10:00 am EST 11/12/2012 we posted an analysis using the same methodology as this post to locate the epicenter of earthquake in Eastern Kentucky over the weekend.

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 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.  The 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

A couple of findings from this analysis

• 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.

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.

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.

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

-----------------

[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) 

------------------------------------------------------------

(# of ALL Tweets in State / # of ALL 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.

Where are all of the "binders full of women"?

Like Mitt Romney in Tuesday night's debate, we were wondering, where are the "binders full of women" applying to work at FloatingSheep?

So, in typical FloatingSheep style, we found a very talented woman to make a map.  Montse Compa, the Humboldt State University student that produced a map of Big Bird tweets during the last presidential debate, helped us answer this question:

So, despite the many memes devoted to binders full of women and the news coverage of these "viral" memes, there are no women actually in binders. Women live in the material world. But just as Mitt Romney is able to represent women as being in binders, there are plenty of women (and people who like women) on Twitter producing counter-representations, as UK Geography grad student Ryan Cooper discovered with this map of tweets referencing the latest presidential debate screw-up.

UK election cyberscapes

In anticipation of the upcoming election in the UK, we have decided to explore the geographies of election-related references in the British Isles. The map below visualises which of five political parties contain the most references at any particular location in the Google Maps database.

References to UK Political Parties

First, a brief note on method. We searched for the three major political parties (Labour, Conservatives and Liberal Democrats) at each location, as well as two of the parties on the far-right of the political spectrum (UKIP and the BNP) that have made gains in recent years. We also searched for the terms "tories + election" and "lib dems + election" and assigned a dot to either the Conservatives or Liberal Democrats if either one of those terms had the most hits at any location.

The map reveals some interesting online political geographies. The Tories score better than any other party. In fact, 61% of locations possess more references to the Conservatives than any other political party, whereas 33.8% of places have more references to Labour and only 3.4% for the Lib Dems.

The UKIP has a particuarly strong showing in the South West, with multiple points that contain more references to "UKIP" than any other party. The BNP do best in South Wales, West Gloucestershire, West Yorkshire and South Tyneside.

One of the most interesting aspects of the map is the degree to which it diverges from maps of likely voting patterns of constituencies. Some of the differences can likely be explained by the relatively recent boost in the polls to the Liberal Democrats (which hasn't yet had a chance to be reflected in material indexed by Google Maps). The strong showing by the Tories could also perhaps be attributed to a greater degree of online engagement by that party.

Another way of gauging online popularity of political parties before the election is to search for the names of each party leader throughout the country. Here we again chose the leaders of the three main parties, as well as Nick Griffin (BNP) in order to explore whether this method can tell us anything about the popularity of the far-right in different parts of the country. The map below shows these results.

References to UK Political Party Leaders

Here we see that Labour's Gordon Brown outperforms his rivals in almost every part of the country, a fact that likely owes much to his current position as Prime Minister. The only significant anomaly seems to be a large number of references to David Cameron in Oxfordshire. Nick Clegg and the Lib Dems again show poorly in this map, although it will be interesting to see how the online visibility of these figures changes after the election.

References to Nick Griffin unsurprisingly appear in many of the same places in which there was also a great deal of visibility for the BNP. We explore the visibility of far-right parties in some more detail through the following maps, which display total number of references to the BNP and the UKIP (this time not compared to any of the other political parties).

References to the British National Party

References to the UK Independence Party

These maps seem to indicate that there is not always a greater total number of references to the BNP or UKIP in places in which they scored highly in the first two maps. In some places, such as West Gloucestershire, it could simply be that there are fewer online references to any of the mainstream political parties.

Are these maps predictors of election results and likely voting patters? We doubt it, but it is nonetheless interesting to observe the very unique geographies occupied on the Internet by different segments of the political spectrum. We will, however, claim any credit for correctly predicting an election result of 61% Tories, 33% Labour and 3% Lib Dems.

Where in the world is Barack Obama? (and John McCain, too!)

To follow up on our previous map showing the difference in the number of mentions between Barack Obama and John McCain in user-generated Google Maps content prior to the 2008 US Presidential Election, we figured an alternative visualization might be beneficial. The following maps represent the absolute number of mentions of Obama and McCain, respectively, in user-generated placemarks, a disaggregation of the map in our previous post.
This map, much like the previous iteration, shows the vast concentration of user-generated placemarks mentioning Obama in the nation's urban centers. The nation's largest cities - New York City, Los Angeles and Chicago - all appear prominently in this map. Although many of the notable points in both the Obama and McCain maps can be attributed to the large populations (and thus, presumably, a greater level of connectedness), a number of other explanations remain necessary. Despite being the 3rd largest city in the United States, Chicago is also the home of Barack Obama, and it houses the highest concentration of placemarks that mention his name. Significant events also seem assert their presence spatially, as Denver, Colorado, the site of the 2008 Democratic National Convention, is another relatively well-represented area, along with Portland, Oregon, where 70000+ rallied for Obama in May 2008.
Mirroring the already established pattern of urban primacy, much of McCain's presence is concentrated in the nation's urban centers, again including both New York City and the Washington, DC metro area (where McCain has the highest concentration). Unlike Obama, the places McCain is best represented in Google Maps were not necessarily the places he fared the best during either the primary or general election. For example, both Iowa and Michigan, in which McCain receives a nearly uniform number of mentions across the state, voted against him in both the primary and general elections.

Despite some of these patterns of user-generated content merely confirming the primacy of urban areas in virtual representations of the material world, others depart significantly from the predicted spatial clustering. Some areas that voted for McCain feature more prominently in the user-generated representations for Barack Obama, and vice versa, with the number of mentions for Barack Obama being more than double the number of mentions for John McCain. Although not all of the patterns displayed can be easily attributed to a particular causal factor, they only further complicate the relational geographies of the virtual and material world.

Google Mapping the 2008 US Presidential Election

Despite being highly contentious, the 2008 US Presidential Election resulted in an overwhelming electoral college victory by President Barack Obama. This map shows the difference in the number of mentions of Barack Obama and Republican candidate John McCain in user-generated placemarks indexed by Google. This peer-produced representation is remarkably similar to more official cartographic representations of the final election results, with a couple of notable exceptions.

Because placemark concentration is correlated with large urban populations, even the states that overwhelmingly voted for Senator McCain seem to favor Obama. This concentration of placemarks in urban areas show a significant advantage for Obama, mirroring his successes during the election. Another anomaly is the red clustering in New Hampshire, a state in which Obama defeated McCain 54%-45%. However, this cluster can be explained by McCain's momentum-building primary win in the Granite State, which eventually propelled him on to the GOP nomination.

Following J.B. Harley (1988), we should also take interest in the silences of this map. Here the primarily rural areas contain either no user-generated placemark information or an equal number of mentions for both Obama and McCain, but nonetheless appear uniformly devoid of content.