Mapping Racism Through Digital Media

With the re-election of President Obama, white people who rooted for the other guy took to various forms of digital media and unleashed their disappointment. Some white folks went a good deal farther than disappointment into overt racism, like this white woman from California who posted her racism on Facebook.  Jezebel pulled together a rogues’ gallery of racist tweets.

The gallery at Jezebel prompted some geographers to create a map of all the racist tweets.

The enterprising folks at Floating Sheep used software they created called DOLLY to collect geocoded tweets for the week beginning November 1. In other words, it’s possible to search Twitter by both location and key word (some other examples here). If I understand it correctly, the DOLLY software allows this search process to be further refined to get data at a more granular level.

What they came up with is a map that allows us to understand, at a glance, how these everyday acts of overt racism are spatially distributed in the U.S.

(Map from Floating Sheep; Interactive Map here.)

This is valuable work and just the kind of thing that I’d think sociologists would be interested in doing (but I digress, slightly). The methodology here, buried in the footnotes on the original post, is a worth exploring a little further.

The research questions they pose are: “Are racist tweets relatively evenly distributed?  Or, do some states have higher specializations in racist tweets?”

To answer these questions, they sampled the universe of tweets.  Specifically, they: “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.”

Following that, they took the number of “hate tweets” by state and divided by the total number in the U.S., that became the numerator. Then, they got their denominator by doing the same for all the tweets in the state, divided by all the tweets in the U.S., which is easier to understand expressed as a formula:

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

(# of ALL Tweets in State / # of ALL Tweets in USA)

Based on this, they assign a number, or a Location Quotient (LQ), for “Post Election Racist Tweets.”  They then rank order states based on their LQ’s.

The results they end up with (Alabama, Mississippi, Georgia end up with 3 highest LQ scores) are less interesting than their map and clever methodology. In their analysis, the writers do well to note that the “prevalence of post-election racist tweets is not strictly a southern phenomenon,” but the ranking of the LQ scores by states makes the opposite case.

I want to suggest here that the problem is two-fold: 1) the way the research question is posed and 2) the state-level of analysis.

The researchers here frame their question in terms of state boundaries and posit something of a false dichotomy between “even distribution” of racist tweets on the one hand, and, “states that specialize” in racist tweets on the other hand.  As anyone who has taken an undergraduate methods class can tell you, this research question shapes the kind of data collection you do, and the analysis you come up with at the end.

The state-level of analysis here is something of a distraction. I understand that since we just went through a presidential election, people are thinking in terms of “states” – swing states, blue states, red states, who carried the state – but here, it makes less sense.

What I see when I look at this map are population centers. Take my home state, of Texas.  The red dots there are clustered around places where there’s population density – Houston, Dallas, and more along the I-35 corridor.  And, compare that to where I live now, on the East Coast. There are red dots all along the Northeast corridor of I-95. At a glance, it looks like racist tweets are not evenly distributed across the U.S. but are concentrated where white people live.

Again, let me say, I appreciate this work immensely, but I think that the state-level questions are the least interesting, and ultimately least revealing set of questions for mapping racism through digital media. Instead, I’d be interested in seeing some other basic demographic info about percentage of white people in the population and the proportion of racist tweets. My guess is that the LQ is highest where there is the highest proportion of white people, but that, as academics are so fond of saying, is an empirical question worth investigating.

More in posts to come on calling out racism in digital media, and the growing backlash against it.

Racist Texting: Examples of Backstage Racism

The other day I was at lunch with my best friend. As we were laughing at the minuscule things childhood friends find amusing, we were interrupted by his beeping cell phone. He had received a forward text from a friend. After reading, he sighed and said, “Take a look at this crap,” as he quickly handed me his smart phone. The message read, “What would you get if Sammy Davis Jr mated with Bo Derek? Answer: A 10 of spades.” His phone suddenly beeped again notifying him of an additional message. Another person attached to the original text then forwarded the list of friends another joke. It read, “A Mexican and a nigger are riding in car . . Who’s driving? A cop!” After my immediate reaction of anger, I asked him if this was the first time he has received racist text forwards. He noted that, “These groups of guys send stuff like this all the time. I just delete them.” The interesting fact is that I knew his other Midwestern small town White friends since I was in high school. They always seemed to go out of their way to greet and talk to me whenever I saw them in public. I sensed they were not the most enlightened fellows, but my Black “spidey senses” never went into overdrive when I was in their presence.

What few people know is that this type of behavior is worldwide. For example, a UK Councillor was recommended for “equality and diversity training” for forwarding racist jokes on his cell in June 2010. In July 2005, four policemen in the UK were fired for exchanging and sharing racist text messages. Also in the UK, it was first reported in 2008 that a service called 118-118 Joke Service, sent out daily jokes that included racist jokes to its subscribers. A Muslim student, Kameron Abbas, then 21, received the following:

1. What’s the difference between ET and an Asian? ET got the message and went home.
2. How do you save a drowning Pakistani? Take your foot off his head.

With little research, I ran across several websites that one could draw from in order to send very racist jokes to friends . The most ridiculous and asinine comment made on one page asserted “Please note that these nigger jokes are only for information purpose. These are not meant for any sort of controversy or to hurt anybody’s feelings. A joke is a joke. If you are easily offended, we suggest you not to read these jokes.” The use of texting and forwarding offensive racist jokes is simply an example of the 21st century “Backstage Racism”. With the political circus that is evolving, racist evidence has been shown on Facebook and Tweeting. People such as Sarah Palin and Republican Minnesota State Senate candidate Mike Parry. Just last week, the national news reported the homophobic Facebook rants from an Arkansas Public School Board memberthat advised that “It pisses [him] off though that we make special purple fag day for them. I like that fags can’t procreate. I also enjoy the fact that they often give each other AIDS and die.”

Leslie Picca and Joe Feagin explain and discuss how racial attitudes and behaviors demonstrated by Whites in private settings are more freely expressed with racially like peers. The fact that everyone attached to my best friends forwarded text were White, exemplifies this line of thinking. Moreover, this example illustrates that the white racist framehas indeed added new mannerisms and techniques that facilitate century-old White ideologies toward marginalized populations in a period of time many blindly called “post-racial.” (On the dramatic expansion of racist activity to cyberspace, see Jessie’s pathbreaking Cyber Racism book.)

Sadly, we who are conscious and familiar of the many faces of racism and oppression cannot simply take the road of “deleting” as my best friend has traveled. We must confront these people and simply state, No. No, we will not be a part of this frame. No, I will not allow the marginalization of any people. Even if we upset those were speaking to, we must take a stand. To me, if I lose a so-called friend, it is simply one less person I would have to account for on my Christmas list. More money…more money. But I digress, in regards to my personal story, instead of wasting my breath of explaining racism, the white racial frame and its impact, marginalization, conflict theories, and matrix of domination to these leptons. I decided to send them a text of my own from my phone to the so called leader in the forwards. It read, “How do you get a racist to laugh on Sunday? Tell them the joke on Friday.”

“Guess I’m a Racist” : Anti-Health Care Ad

In the last day or two, an “unknown political group” has created a video (and loaded YouTube), called “I’m a Racist,” and it’s been getting a lot of attention. The short description posted with the video states ‘We believe the health care system needs to be fixed. However, government intervention is not the answer, nor should we be called racist for not agreeing with Obama’s health plan!’ Fortunately, Rachel Maddow and Melissa Harris-Lacewell, provide a thorough critique in this clip (8:01):

Harris-Lacewell makes an excellent point here when she points out the way the ad reinforces an individualized notion of racism, as a personal trait, rather than an understanding that racism is systemic.

This “Guess I’m a Racist” meme jumped to Twitter and people began updating using the hashtag #youmightbearacist. (Using hashtags (#) on Twitter is just a way for people to have a conversation around a theme, so on an evening when the BET Awards are on, people might use #BET as a hashtag to talk about the awards. But the racism prompted by that hashtag is another story.)

Some of the updates to Twitter with the #youmightbearacist hashtag were meant to be funny and skewer racism, some were not so funny deeply racist. Almost all reinforced the point that Harris-Lacewell makes about the anti-health care ad, which is that they assume that racism resides in an individual rather than operates systematically.

There are a couple of things that are interesting about all this for me. First, the video opposing health care is a fairly slick politlcal ad yet it’s created by an “unknown” political ad. In this way, it’s similar to the cloaked sites that I’ve written about here (and in my recent book, Cyber Racism) in which people disguise authorship of websites in order to conceal a political agenda. This ad is slightly different because it’s being pretty overt about part of their political agenda (opposing health care reform), but because the identity of the group that created the ad is hidden, we don’t know how their stance on this one issue may (or may not) be part of a larger political agenda.

What intrigues me further about this is the convergence and overlap of media. So, the unknown political group releases a video on YouTube exclusively, and the video quickly goes viral and becomes one of the most viewed videos on YouTube. They do not buy air time on television to get their message out, but they don’t have to, because the video gets picked up by Maddow’s show and she airs the video. Then, the meme travels to Twitter, where people both reinforce and resist (sort of) the notion of what it means to be “a racist.” The political battle over race, and the meaning of racism, has moved into the digital era.