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

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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) 

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(# 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.

Can Twitter Predict the US Presidential Election?

Can Twitter predict the outcome of tomorrow's US presidential election? If the results of our preliminary analysis are anything to go by, then Barack Obama will be easily re-elected. The data presented below, including all geocoded tweets referencing Obama or Romney between October 1st and November 1st, out of a sample of about 30 million, give some insight into the visibility of each of the candidates on Twitter.

We see that if the election were decided purely based on Twitter mentions, then Obama would be re-elected quite handily. In fact, the only states in the electoral college that Romney would win are Maine, Massachusetts, New Mexico, Oregon, Pennsylvania, Utah, and Vermont. Romney also wins in the District of Colombia, and we unfortunately didn't collect data on Alaska or Hawaii. Some of the results seem to be interesting reflections of social and political characteristics of particular places. It makes sense that Romney has captured more of the public imagination in Utah, likely due to the state's considerable conservatism and large Mormon population, and Massachusetts, the state that he governed not all that long ago.

However, this drubbing that Romney receives in the Twitter electoral college belies the close nature of the final popular (Twitter) vote, re-raising the issue of whether the electoral college is the most suitable means of deciding the country's political future. There are a total of 132,771 tweets mentioning Obama and 120,637 mentioning Romney, giving Obama only 52.4% of the total and Romney 47.6%, a breakdown that is remarkably similar to current opinion polls, though not reflected when looking at the state-level aggregations in absolute terms. If you want to explore the data in more detail, please play around with the interactive map below:

We can also visualize the data using a sliding scale, so as to see how close the margin of victory is for each candidate in a given state.

Romney's largest margins of victory are in Pennsylvania and Massachusetts, while Obama's largest victories are in California and, strangely, Texas. The cases of Massachusetts and Texas, not to mention large portions of the south and plain states, likely point to the fact that many references on Twitter would tend to be negative.

It is also worth noting that we compared Twitter mentions of both Vice-Presidential candidates: Biden and Ryan. Ryan, interestingly, wins the head-to-head competition in every single state. This makes for a rather boring map, so we decided to instead compare references to Ryan and Romney in the map below (Romney shaded in grey for his ebullient personality, and Ryan in pink as a result of his staunch support for gay rights).

As might be expected, there are more references to Romney in most states (Kansas, Michigan, North Dakota, Rhode Island, South Dakota, and Vermont being the exceptions here). However, when looking at total references, we again don't see a large gap between the two men. Ryan has 94,707 tweets compared to Romney's 120,637.

What do these data really tell us? Ultimately, I doubt that they will accurately predict the election, as Obama's seeming victory in Texas or Romney's in Massachusetts will almost certainly not come to pass. But they do certainly reveal that many internet users in California, Texas, and much of the rest of the country for that matter, tend to talk more about Obama than Romney. And, of course, in order to truly equate tweets with votes, we would need to employ sentiment analysis or manually read a large number of the election-related tweets in order to figure out whether we are seeing messages of support or more critical posts, as has been done in a couple of interesting projects by Twitter available here and here and another project by Esri available here.

Maybe the most revealing aspect of these data is that the 'popular vote' is split between the two candidates. While the social and political data shadows that we are picking up may not accurately tell us much about the electoral college results, when aggregated across the country they may be a rough indicator of tomorrow's outcome, pointing to the more-or-less equal and evenly divided nature of the American two-party political system. While this work may seem like a contemporary attempt at soothsaying, something we tend to shy away from, the data more appropriately serve as a useful benchmark in order to allow us to analyze what social media data shadows might actually reflect, as no matter the level of participation, they remain distorted mirrors on the offline material world.

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.

Visualizing Twitter commentary on the 2012 Presidential Debates

Here at the Floatingsheep virtual compound (located somewhere in wilds of information space between 163.1.201.42 and 137.150.145.240) we are avid followers of the changing trends with culture and politics, particularly as they manifest in the online world.  So it should come as no surprise that we have been tracking the U.S. presidential election over the past months. We were particularly interested by the extent to which Twitter featured in media coverage of the first presidential debate and wanted to take a look at the geography of debate tweets. Moreover, given our general solidarity with all farm yard animals we also wanted make sure we had Big Bird's back [1].

So we fired up the interface to the DOLLY project, which just archived its billionth geocoded tweet last week, to take a look.  By the way, if you are interested in working on maps like this yourself, be sure to check out the Sheepallange.

But before getting to our work let's take a look some other non-geographic work.  The debate was clear a trending topic on Wednesday night with over 10 million tweets sent and this temporal dimension is well illustrated by the graph below and the analysis of Twitter itself. While there are issues with the representativeness of the Twitter universe, it is useful metric to watch.

As geographers, however, we wanted to examine the spatial dimension of these tweets, particularly with respect to the handful of swing states (according to CNN) that have are key in the upcoming election. So we commissioned, at great expense, a series of maps created by Monica's cartography students at Humboldt State University [2]. The goal was to demonstrate the geographic expressions of online political engagement as evidenced by debate-related tweets.

 Catherine Hoyle, a Humboldt State Oceanography major, looked at where people geotagged tweets for Obama or for Romney.

Stephen Mangum, a Humboldt State Geography major, examined the tweets declaring either "Obama won" or "Romney won" in relation to the political leaning of the state.

While the maps above are certainly illuminating, truth be told they skirt the key issue of the candidates' stances on the future of Serinus Canaria Sesamestreetous, with an apparent glandular disorder resulting in extreme size, i.e., the attack on Big Bird by Mitt Romney. We stand in solidarity with our feathered friend, who is a long time advocate of the sheep community. As the video clip below demonstrates, Big Bird has regularly and eloquently spoke out for sheep. "Are you worried about sheep like I am? Well I've been thinking about it a lot, so I wrote a poem, and I'll read it to you so you'll see what the problem is here."

Big Sheep by Big Bird

The Sheep in smaller than a bull

Her nose is black, her coat is wool

We cut her wool off and upset her

To make into a woolen sweater

When winter comes and snowflakes float

She could be could without her coat

So let's be fair when snow is deep

Let's put the sweater on the sheep

Montse Compa, a Humboldt State Environmental Science major, was also worried about Big Bird's employment prospects if Romney wins the election (as Twitter predicted in Stephen's map).  On this map the larger the size of the Big Bird the more Tweets during the debate about Big Bird.

But sometimes a map is not enough, so like our hero Big Bird we turn to poetry as well...

Big Bird by the Big Sheep (aka Matt)

The Bird is bigger than a sheep

Her feathers are yellow, she lives on the street

We threaten her funding and upset her

And worse, make her a debtor.

When winter comes and snowflakes float

She could be insurance-less, with a sore throat

So let's be fair when snow is deep

Let Big Bird, her money keep 

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[1] OK, technically a canary is not a farmyard animal, but where else are you going to be able to put Big Bird?

[2] Actually there was no expense in this. It just sounds better that way.

Mapping Cyberscapes of the 2012 Republican Presidential Primary

They've given us gems like "I like being able to fire people", suggesting that we replace professional janitors with dozens of children from working-class homes in order to cut costs. And a bunch of other crazy inventive stuff. In an indirect way, they've also given us new vocabulary words, parodies and re-interpretations. They've also provided the raw material necessary for a range of user-generated, web 2.0, prosumptive behavior. So even though you may be a little bit frightened, you should also thank them -- albeit not necessarily with your vote.

But with the Republican presidential primaries already well underway (and today being the Florida primary), we thought it a good time to dig a bit deeper than the superficial soundbites coming from the candidates. So in this post we're understanding the geography of these candidates via pythagoric numerology and haruspicy. Ha! Just kidding, we will be looking at the distribution of geotagged online content like always. After all that's the whole point of the blog and something we've done previously for European political leaders, as well as the 2010 election in the UK and the 2008 US Presidential election. Just sometimes we dream about a change....

So what are the geographies of the 2012 GOP primary like? Is it possible for these cyberscapes to help us predict election outcomes? Are they total hogwash? Just pretty colors?
Mapping references to each of the original eight GOP contenders, one sees that the two current front runners for the nomination, Mitt Romney and Newt Gingrich, actually have very few references relative to the other candidates. It seems evident then that these cyberscapes show a lesser degree of differentiation between candidates than is evident in the primary results thusfar.

One of the clearest patterns is the plethora of references to Rick Perry across his home state and the very few outside of Texas. Given Texas' minimal influence on the nomination process, it's likely that he now wishes he was from Iowa. There is a similar, albeit much smaller, pocket of references to Jon Huntsman in his home of Salt Lake City, while Minnesota has a cluster of references to US Representative Michele Bachmann.

But most evident is the vast swath of territory, with no real conformity to political borders, that is dominated by references to Ron Paul. Libertarians are everywhere! It's nice that the federally funded national highway system is there to help speed their movement!

Even removing the candidates that have now dropped out of the race, Ron Paul's dominance in the cyberscape of the Republican primary field is evident. While Paul's prevalence in geocoded references isn't reflected in the polling numbers in the real election, it isn't entirely surprising. As we saw over two years ago with Barack Obama's disproportionate prominence in Google Maps content, Ron Paul's prominence online is certainly a reflection of his campaign's use of the internet as a primary organizing tool.

But since the electoral system in the US is really so dependent upon what happens at the state-level we thought it worthwhile to stray from our usual method of measuring cyberscapes on a more flexible, point-by-point basis and instead aggregate references on a state-by-state basis [1].
It doesn't seem, however, to make much of a difference in the relative visibility of the different candidates. Ron Paul's seeming dominance over the virtual landscape remains a fact, while Mitt Romney wins only his home state of Massachusetts as well as Utah and Alabama and Gingrich winning just his home state of Georgia. Santorum's "win" in Oregon is primarily due to his "Google problem", with numerous references to the alternative meaning in Eugene [2].

So, if one were to use this map as a prediction of victories in GOP primaries, Ron Paul would easily be the next Republican presidential candidate. Indeed, according to references alone, Ron Paul would have won each of the three primaries that have already taken place (of which he actually won zero).

This aptly highlights the difference between online activism and offline activism. Not that we really needed a reminder after all the protest events of last year. Moreover it will be some time (thank goodness!) before Google Maps can be used to predict presidential elections. Although we're sure that someone is developing an app as we speak.

But one thing is clear, based on these maps we feel that there despite his love of conspiracy theories about the New World Order, Ron Paul might actually be the one controlling the internet.

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[1] States shaded grey are representative of no clear "winner" in the number of geocoded references to the candidates. Either there were no references to the candidates' names or at least two candidates were tied for the greatest number of references -- essentially the same reasoning as the many points with no dots on the other maps above.
[2] Which brings up the role of Google and code in how places are represented online.