Mapping Ferguson Tweets, or more maps that won't change your mind about racism in America

This post is the culmination of the Inaugural #IronWilson Map-a-Thon, held on Saturday, August 16th, and is the result of a collaboration between Matthew Wilson, Eric Huntley, Ryan Cooper and Taylor Shelton. 

A little over a week ago, the streets of Ferguson, Missouri, a suburb of St. Louis, were disrupted by the shooting of 18-year-old Michael Brown by Ferguson Police Officer Darren Wilson. While the details have been slow to emerge, the reaction to the killing of yet another unarmed young black man has been anything but -- whether in the form of street protests in Ferguson, or the online reaction to the news as seen on Twitter. The following graphic, produced by Twitter and published online by the Washington Post, demonstrates a typical representation of what we might call ‘#hashtag frenzy’, as people around the country take to Twitter to react to and comment upon the news. 

While certainly flashy and eye-catching, these so-called ‘animated ectoplasm maps’ tend to be short on meaningful insights. These visualizations show little more than population density in the US, and are remarkably similar from one trending topic on Twitter to the next. There is no attempt to normalize the data by population or overall levels of tweeting in a given place, thus obscuring both more detailed spatial patterns and broader social meanings that might be drawn out of such data. Still, maps such as this are useful in demonstrating the waxing and waning attention span toward issues of social importance, including the registering of yet another gun-related and police-initiated violent event, something that this blog post itself contributes to; therefore, in full admission of ‘yet another Twitter map of racial violence’...

We collected all geotagged tweets referencing a series of keywords -- 'Ferguson’, ‘handsup’, ‘mikebrown’, ‘dontshoot’ and ‘handsupdontshoot’ -- from Saturday, August 9th when the shooting occurred through the morning of Friday, August 15th, in an effort to provide a bit more resolution and, hopefully, insight into the ways and places people were tweeting about the protests. Starting with the first geotagged tweet referencing the shooting, we collected a total of 38,450 tweets. 'This tweet came from user Johnny__Tapia at 3:11pm Central Time on Saturday, saying “Ferguson police just shot a kid in the head in the middle of the street. 17 yrs old. Ain’t nobody saying what he did” [1].

In the several days following the shooting, news spread quickly over Twitter, with social media providing a key source of updates and information in lieu of any official reports or communication from the Ferguson Police Department. The map below, made by Eric Huntley, aggregates all the tweets in this dataset to hexagonal cells across the continental United States, and normalizes it relative to the overall amount of tweeting in that location at the same time. In other words it shows the relative focus of tweeting related to Michael Brown’s shooting (and the subsequent protests and police crackdown) compared to overall tweeting activity by location. In this map lighter shades indicate relatively more tweets about Ferguson than the national average.

The national and international media coverage of the story in Ferguson points toward the notion that this event transcends the local; there is something about it that speaks to people from any number of places and walks of life. For example, the aforementioned WaPo article ends with the relatively meaningless maxim, “People are watching from as far away as Fiji and Ghana. That's the world we live in now.” While discussions of increased police militarization and the persistent legacy of racism have certainly resonated strongly with a national audience, it is evident from our more-than-just-dots-on-a-map approach that the tweeting around this event is actually most prevalent in the general vicinity of where the shooting occurred: the St. Louis metropolitan area. The proportion of tweets on the topic is higher in and around St. Louis than anywhere else in the country, while other cities around the country have largely continued about their business, with lower levels of Ferguson-related tweeting relative to overall levels of Twitter activity [2]. While a few scattered and isolated areas throughout the country demonstrate a relatively high amount of tweeting about Ferguson -- mostly as a result low overall levels of tweeting -- the St. Louis region is really the only place that demonstrates a particularly concentrated and significant interest in the matter. In other words, "the world we live in now" is one in which spatial proximity and social connectedness remains incredibly important, even if people in Fiji and Ghana can follow along, too.

This lies in contrast to the aftermath of George Zimmerman receiving a ‘not guilty’ verdict last summer in his trial for the shooting death of Trayvon Martin, which is arguably the best parallel in terms of the public outcry and attention to the current Ferguson situation. Following Zimmerman’s verdict, large portions of the American South demonstrated a greater likelihood to use the #JusticeForTrayvon hashtag than other parts of the country, which we interpreted as indicative of Twitter users making connections between the events in Sanford, Florida and the broader legacies of racialized violence throughout the American South. Whether the different geographies of Twitter's reactions to these events are the result of different temporal evolutions (the immediate aftermath of the shooting vs. the trial verdict a year and a half later) or in divergent experiences, or perceptions, of racism between the South and Midwest [3], or something else entirely, is left to some level of speculation.

Despite the overall concentration of tweets in the St. Louis region, it also important to recognize that spatial unevenness exists at multiple scales, with respect to practically any phenomena. Indeed, the ability to examine such phenomena at a variety of scales is one of the major advantages to aggregating these points -- or individual tweets in this case -- to a uniform grid of hexagonal cells, as opposed to the more conventional, and largely arbitrary, Census-defined areal units. In the GIF below, created by Matt Wilson, you can see the spatial distribution of raw (i.e., non-normalized) tweets -- using the same dataset -- in the St. Louis metro area over time, beginning on Saturday when the shooting occurred, through the end of Thursday, August 14th [4].  

Given our lack of first-hand knowledge of St. Louis and its environs, we’re hesitant to draw too many conclusions from this data, though we certainly welcome any potential explanations from our readers. Because each of these snapshots is classified in the same way, we can see the diffusion of the news and growth in interest over time, becoming much more pronounced beginning on Monday. Tuesday is interesting insofar as it seems to demonstrate a much stronger clustering around Ferguson itself (the cluster of three dark blue hexagons north of downtown St. Louis), with the rest of the city actually seeing a decrease in tweeting about the event. This interest, especially in downtown St. Louis, ramps back up on Wednesday and Thursday, around the time of growing protests and the increasingly violent response from the Ferguson Police.

Ultimately, despite the centrality of social media to the protests and our ability to come together and reflect on the social problems at the root of Michael Brown's shooting, these maps, and the kind of data used to create them, can’t tell us much about the deep-seated issues that have led to the killing of yet another unarmed young black man in our country [5]. And they almost certainly won't change anyone's mind about racism in America. They can, instead, help us to better understand how these events have been reflected on social media, and how even purportedly global news stories are always connected to particular places in specific ways.

----------
[1] It appears that this user has since deleted all of his tweets back to July 10.
[2] There is still a significant absolute amount of tweeting in these places, there just also happens to be a generally massive level of tweeting about other topics, as well.
[3] Of course, St. Louis, like pretty much everywhere in the United States, has it’s own important legacies of racism. For example, please see: Deep Tensions Rise to Surface After Ferguson Shooting, The Most Racist City In America: St. Louis?, and The Century-Old Urban Policy That Divides St. Louis.
[4] These maps also use a somewhat smaller sample of tweets that have only exact latitude and longitude coordinates, so as to avoid using those tweets tagged to place names, such as ‘St. Louis’, which might give the impression that there were large contingents of tweeters at the geographic center of the city.
[5] Though data about racial profiling, as Ryan Cooper analyzed for us here, can point towards some potential explanations.

Tweeting for Trayvon

While the not guilty verdict is in for George Zimmerman, the discussion about and ramifications of Trayvon Martin's killing seventeen months ago are only beginning, from protest marches throughout the country to tweeting with hashtags like #MillionHoodies. Plenty of people smarter than us have weighed in on what this means for the persistent racism and inequity of the justice system in the United States, so we'll leave that side of the analysis to them. But as we specialize in thinking about and analyzing the geographies of social media, we want to offer our own two cents on what we can collectively take away from the case based on an analysis of geotagged tweets reacting to George Zimmerman's acquittal for Trayvon Martin's slaying.

First, some quick notes on our methodology and general trends in the data. Using DOLLY, we collected all the geotagged tweets from July 1 through July 15, referencing either "JusticeForTrayvon" or "Not Guilty", capturing the usage of these phrases with or without an accompanying hashtag. There were a total of 27,863 tweets referencing "Not Guilty" in this time frame, and just 6,614 referencing "JusticeForTrayvon". We calculated location quotients using hexagonal binning in order to normalize the data based on a relative measure of tweeting activity, as well as to account for differential size of counties or other similarly arbitrary areal units [1]. More simply, this allows us to compare the relative level of Twitter activity in any particular location, rather than relying on raw counts which are biased by population density.

Timeline of Tweets Referencing "Trayvon" from July 13th-14th

In addition to our primary interest in the spatial dimension of tweeting, we're also able to visualize a timeline of tweeting activity, which shows a clear spike immediately following the verdict on Saturday evening around 10 pm. While we're sure that many people's timelines were filled with reactions to the verdict throughout the day on Sunday, it seems as though much of the tweeting became more dissipated throughout the day as protests heated up and others went back to their usual routines.

Taking a look at the spatial patterns of these keywords, there are some clear differences. While there are many fewer JusticeForTrayvon tweets overall, they tend to be generally scattered, but with some relative concentrations largely in the south, in cities like Shreveport and Alexandria, Louisiana and Durham, North Carolina. Again, these measured are normalized for overall level of Twitter activity and thus show that these places were more engaged in this topic via Twitter than other parts of the country.

References to Not Guilty, however, in addition to being far more prevalent, demonstrate significantly more clustering in areas of the country outside the south, especially in Texas (depending on whether or not you consider it to be Southern) and some of the Midwestern or Mid-Atlantic states. We should note that there is a greater concentration references to Not Guilty in the vicinity of Sanford, Florida, the location of Trayvon Martin's killing and the subsequent trial, than was visible in references to JusticeForTrayvon. 

It is also important to note, however, that large cities on the west coast, like Los Angeles, San Francisco and Seattle, have relatively little tweeting about the case for either term, as do major cities along the eastern seaboard, like New York, Boston, D.C. and Philadelphia, despite being the sites of the major protests following the verdict.

Comparing references to the two terms -- while keeping in mind that they are not entirely oppositional, i.e., "Not Guilty" is a much more neutral and contextually dependent phrase than JusticeforTrayvon, which explicitly 'takes sides' in this debate -- reveals a much clearer geographic pattern. This comparison brings the different geographies of these phrases into a stark contrast, with many more references to JusticeforTrayvon concentrated throughout the southern states of Arkansas, Louisiana, Mississippi, Alabama, Georgia and Kentucky (highlighted in purple), with a greater number of more generic references to the verdict (highlighted in green) scattered throughout much of the rest of the country. In short, the hashtag that is more closely associated with protesting the outcome of the court case, is more highly concentrated in Southern states.

One thing that is clear is that although the experience of racism isn't unique to the American South, it is uniquely associated with and experienced in that place when viewed through geotagged social media content [2]. This isn't to say that the tweeting about the case throughout the south is, in and of itself racist, as many, if not most, tweets express outrage at Zimmerman's acquittal, as evidenced by the large number of tweets referencing the JusticeForTrayvon hashtag. But given the back-and-forth around the particularity of racism in the south or the universality of racism across the United States, the higher concentration of this Twitter discussion within the region suggests a process distinct from the rest of the country.

The fact that Trayvon Martin's killing took place in Florida, which shares a similar history with regard to race as the rest of the south, has clearly elicited a broader reaction from those in a (relatively) similar geographic context. The complexities of racism (both historical and contemporary) as expressed in part through problematically-enforced laws like stand-your-ground come to the fore in the south at a time like this, as can be seen in the much higher-than-usual tweeting about the case in Alabama, Georgia, Mississippi, Louisiana and Arkansas. If anything, the outpouring of tweets throughout the south in support of the Martin family and in favor of a more sensible and equitable justice system serves to destabilize the common narrative that the south is unitary, coherent region populated by those clinging to nineteenth century racial mores. The south is, like any other place, marked by conflict and contradiction, something evident nowhere more than in the way it continues to deal with (or ignore) persistent racial inequality like that seen in Trayvon Martin's killing and George Zimmerman's acquittal.
-----------
[1] We've previously demonstrated the utility of this method for mapping concentrations of tweets about a given phenomena.
[2] See, for example, our work on mapping racist tweets in response to President Obama's re-election last November.

The Geography of Hate

UPDATE (5/13/13 @ 10:45pm): We have written and published a FAQ to respond to some of the questions and concerns raised in the comments here and elsewhere. Please review our comments there before commenting or emailing.

Following the 2012 US Presidential election, we created a map of tweets that referred to President Obama using a variety of racist slurs. In the wake of that map, we received a number of criticisms - some constructive, others not - about how we were measuring what we determined to be racist sentiments. In that work, we showed that the states with the highest relative amount of racist content referencing President Obama - Mississippi and Alabama - were notable not only for being starkly anti-Obama in their voting patterns, but also for their problematic histories of racism. That is, even a fairly crude and cursory analysis can show how contemporary expressions of racism on social media can be tied to any number of contextual factors which explain their persistence.

The prominence of debates around online bullying and the censorship of hate speech prompted us to examine how social media has become an important conduit for hate speech, and how particular terminology used to degrade a given minority group is expressed geographically. As we’ve documented in a variety of cases, the virtual spaces of social media are intensely tied to particular socio-spatial contexts in the offline world, and as this work shows, the geography of online hate speech is no different.

Rather than focusing just on hate directed towards a single individual at a single point in time, we wanted to analyze a broader swath of discriminatory speech in social media, including the usage of racist, homophobic and ableist slurs.

Using DOLLY to search for all geotagged tweets in North America between June 2012 and April 2013, we discovered 41,306 tweets containing the word ‘nigger’, 95,123 referenced ‘homo’, among other terms. In order to address one of the earlier criticisms of our map of racism directed at Obama, students at Humboldt State manually read and coded the sentiment of each tweet to determine if the given word was used in a positive, negative or neutral manner. This allowed us to avoid using any algorithmic sentiment analysis or natural language processing, as many algorithms would have simply classified a tweet as ‘negative’ when the word was used in a neutral or positive way. For example the phrase ‘dyke’, while often negative when referring to an individual person, was also used in positive ways (e.g. “dykes on bikes #SFPride”). The students were able to discern which were negative, neutral, or positive. Only those tweets used in an explicitly negative way are included in the map.

Tweets negatively referring to "Dyke"

All together, the students determined over 150,000 geotagged tweets with a hateful slur to be negative. Hateful tweets were aggregated to the county level and then normalized by the total number of tweets in each county. This then shows a comparison of places with disproportionately high amounts of a particular hate word relative to all tweeting activity. For example, Orange County, California has the highest absolute number of tweets mentioning many of the slurs, but because of its significant overall Twitter activity, such hateful tweets are less prominent and therefore do not appear as prominently on our map. So when viewing the map at a broad scale, it’s best not to be covered with the blue smog of hate, as even the lower end of the scale includes the presence of hateful tweeting activity.

Even when normalized, many of the slurs included in our analysis display little meaningful spatial distribution. For example, tweets referencing ‘nigger’ are not concentrated in any single place or region in the United States; instead, quite depressingly, there are a number of pockets of concentration that demonstrate heavy usage of the word. In addition to looking at the density of hateful words, we also examined how many unique users were tweeting these words. For example in the Quad Cities (East Iowa) 31 unique Twitter users tweeted the word “nigger” in a hateful way 41 times. There are two likely reasons for higher proportion of such slurs in rural areas: demographic differences and differing social practices with regard to the use of Twitter. We will be testing the clusters of hate speech against the demographic composition of an area in a later phase of this project. 

Hotspots for "wetback" Tweets

Perhaps the most interesting concentration comes for references to ‘wetback’, a slur meant to degrade Latino immigrants to the US by tying them to ‘illegal’ immigration. Ultimately, this term is used most in different areas of Texas, showing the state’s centrality to debates about immigration in the US. But the areas with significant concentrations aren’t necessarily that close to the border, and neither do other border states who feature prominently in debates about immigration contain significant concentrations.

Ultimately, some of the slurs included in our analysis might not have particularly revealing spatial distributions. But, unfortunately, they show the significant persistence of hatred in the United States and the ways that the open platforms of social media have been adopted and appropriated to allow for these ideas to be propagated.

Funding for this map was provided by the University Research and Creative Activities Fellowship at HSU. Geography students Amelia Egle, Miles Ross and Matthew Eiben at Humboldt State University coded tweets and created this map.

The full interactive map is available here: http://users.humboldt.edu/mstephens/hate/hate_map.html

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.

Have the sheep conquered racism?

One certainly doesn't need to look far to see evidence of the persistence of racism in our world today. And while it may not seem obvious, our kindred sheep (of the non-floating variety) are no strangers to such discrimination. How would it feel to literally be a black sheep? Probably not so good.

In a surprising move, it appears the sheep of the world (or humans, acting as a proxy for their fleeced friends) have made a concerted effort to counteract such pervasive racism in the virtual realm. As the map below shows, at all but around 100 randomly distributed points on the earth's surface, Google Maps references to "sheep" outnumber references to the infamous "Ku Klux Klan".

Sheep contra the Ku Klux Klan

This map, of course, does not take into account the potential that many of these references to sheep are actually related to an ongoing intra-species dispute over whose wool is the finest of all, a dispute indubitably wrapped up in its own forms of racist and nationalist language, thus only perpetuating the racism they have seemingly defeated. If only we could decipher all those placemarks that just say "baaaaaaaaaahh".