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.

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.

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 

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

[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.

Measuring Politicians' Popularity in Google Maps Placemarks

Mapping the relative popularity of different politicians is old hat to the Floatingsheep collective -- our map comparing references to Barack Obama and John McCain was one of the first maps ever featured on the site (and the first that Taylor made!). A much more aesthetically advanced version of that map has now published in the Atlas of the 2008 Elections, edited by fellow Kentucky geographer Stan Brunn and a bevy of others. To honor that publication, as well as to acknowledge the ballyhoo these days about the role of digital technologies in promoting social and political change across the globe, more analysis seems timely. We now broaden the geographic extent of our earlier map and present the following, showing the relative prevalence of references to the names of political leaders in eight major countries in Europe and North America.

Politicians' Placemark Popularity

As is par for the course around here, each color dot represents more references in that location to the name of that politician than to each of the other seven. In other words, a purple dot means that there are more references to Barack Obama than to Angela Merkel, David Cameron, etc. It should also be noted that the keywords used for this comparison are the full names of each politician, rather than simply a last name.

Politician's Popularity in Europe

When focusing on Europe, the map almost perfectly shows that references to the name of a political leader are likely to predominate in the country that politician represents. England is awash in the burnt orange color symbolizing David Cameron, Spain in brown for José Luis Rodríguez Zapatero, France in the pink of Nicolas Sarkozy, the silver of Silvio Berlusconi covering Italy, the blue-green of Angela Merkel filling the borders of Germany, Recep Tayyip Erdoğan's yellow in Turkey and the green of Dmitry Medvedev scattered across Russia, however concentrated in the west. In this sense, the map conveys a relatively simple point that we've been spending quite some time trying to reiterate: the internet, and thus the data within it, is not somehow disconnected from geography. Instead, the two are very much intertwined, with digital representations of place being very much tied to the characteristics of that place, including its politics.

Where this map gets interesting, however, is when one looks away from Europe, especially returning to the United States (see the first map above). One may expect a veritable blanket of purple, symbolizing Obama, to cover the country in much the same way as the references to other political leaders did in their home countries. It is instead a potpourri of colors, with each of the other politicians dominating in one place or another. Whether this has to do with Obama's declining popularity or something else, we are unsure.

Given that all of the other countries included in this map, with the exception of Russia, are relatively small in terms of area, there may be a negative correlation between the areal extent of the country and the likelihood of complete homogeneity in Google Maps references. It is surprising, however, how much this deviates from Obama's dominance when compared to John McCain in 2008, as reflected in our Presidential Placemark Poll map. Maybe this is just evidence of an evil Obama plot to sell off America's virtual territory to socialist (and not-so-socialist) Europeans?

As always, our speculation usually leads us to a dead end, to which we have now arrived. Let the digital jockeying for territorial dominance commence!

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.