States writes


One of the most challenging aspects of anti-racism is the fact that we can only usually measure racism as an absence of a better explanation. We see an inequality and then we try to rule out the other plausible explanations, and then say “it’s got to be explained by racism”. Because there is no objective test – no screen or marker or physical indicator – that positively identifies racist intent (or even racism that happens unintentionally), it is usually left to anti-racist educators to make a case through narrative explanation rather than through empirical observation.

Their (our) task is made even more difficult by the fact that, partially because people are defensive and partially because people are assholes, any claim that racism plays a role in any event is met with a howling chorus of denials and demands for the kind of rock-solid proof that is so rarely available when discussing these kinds of social/psychological issues. When these demands cannot be readily met (‘my racism detector is on the fritz’), these voices devolve into smug pronouncements of ‘race cards’ being played, or perhaps a ‘playing the victim’ gambit being used.

Which is why it’s always interesting and gratifying to see exercises like this one:

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 before we get into the results of the exercise, I want to make the point that this is not the same thing as measuring racism at a state level. Racism manifests itself in a large variety of ways, only some of which are as blatant as the search terms used by the authors (they use “nigger” “monkey” “Obama” “elected” and “won”). There are types of racism that cannot be detected through angry tweets – housing or job discrimination won’t show up in social media, nor will wage gaps, microaggressive behaviours, or underfunded schools. It might be more accurate to say that this is a proxy measure for how comfortable people in different states are with making public and overt racist statements.

That being said, because comfort with overt racism is usually correlated with prevailing racist attitudes, it’s not a stretch to conclude that someone who’s going to tweet about how angry they are that the monkey nigger got re-elected is probably not too bothered by other, non-obvious forms of racism. And a community that spawns such a person is likely a community that has attitudes on race that are, shall we say less than enlightened?

With all that in mind, let’s look at the results:

State LQ of Racist Tweets
Alabama    8.1
Mississippi    7.4
Georgia    3.6
North Dakota    3.5
Utah    3.5
Nevada    0.5
Iowa    0.4
Indiana    0.3
New York    0.3
Arizona    0.2

So a couple of perhaps surprises here, as well as a couple of things that probably don’t surprise you at all. It’s interesting that North Dakota and Utah round out the top of the list, whereas states like Arizona and Oklahoma with recent and major issues with race and racism come in among the states with the lowest quotient. I would expect that states with a long history of racial tension, low average income, and where white and black residents live in relatively close geographic proximity to each other (although not necessarily places where they live/work together) would have more frequent racist tweets.

In the study’s FAQ, the authors specifically deal with some of the major objections to their methodology, including the fact that this only includes tweets that are geocoded (meaning that they are more likely to come from smart phones than from computers), and is thus not necessarily a representative sample of all tweets sent with this content. It is also worth noting the similarities between this exercise and a similar one performed with Google searches, which I’ve blogged about before.

Measuring racism outside of the context of the humanities is a problem that will continue to plague the conversation on race. While psychologists and sociologists are able to make great strides and have been laying the groundwork for a rigorous field of research, the lack of easily-digested numerical references continues to be a stumbling block in getting anti-racist thought accepted into mainstream discussion. I don’t doubt that most people agree that racism exists, but until we can produce findings like these for less dramatic examples than “Obama is a monkey nigger”, we’re going to continue to face an uphill battle from those who would rather deny reality than face it.

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Comments

  1. left0ver1under says

    The tweets from the twits made for…interesting reading, since I don’t want to say anything regrettable. I don’t use twitter so I can’t respond to them, but I would tell them this:

    “If you want to live in a christian-dominated country that has only one political party (a far right wing party), where women are subservient to men, gays have no rights, and only whites have legal status, then I suggest you move to Russia. They’d be happy to have you.”

  2. Jennifer Allen says

    I certainly agree that racism exists, both covert and overt, yet I don’t know how to define covert racism and haven’t seen a good definition or a reliable way to detect it.

    What I do see, I think, is people claiming covert racism without evidence (or adequate evidence)and people denying that it exists in specific cases.

    It would seem to come down to subconscious (or carefully hidden conscious) motivation.

    If I’m correct in the above, how do we proceed?

  3. says

    I don’t know how to define covert racism and haven’t seen a good definition or a reliable way to detect it.

    How hard have you looked, exactly?

    people claiming covert racism without evidence (or adequate evidence)

    Without adequate evidence to convince you personally? Or is there some kind of objective benchmark for how much evidence you have to produce before it is reasonable to start taking a complaint of race seriously?

  4. Pen says

    I’ve seen a ton of convincing evidence of racism (statistical and scientific and all that) and actually you’ve posted quite a lot here. It’s also obviously true that racism doesn’t explain every unpleasant experience visited on any given black person by a some other white person. I think it’s a pretty non-contradictable explanation for racist tweets though.

    I would expect that states with a long history of racial tension, low average income, and where white and black residents live in relatively close geographic proximity to each other (although not necessarily places where they live/work together) would have more frequent racist tweets.

    Based on my personal totally non-scientific experiences, I would expect a lot of overt racism in mostly white areas where that attitude isn’t confronted with, you know, actual real black people or challenged by them directly.

  5. jenny6833a says

    I suspect that those who look the hardest find what they seek to see, just as those who don’t look at all find nothing. I hope that there’s an objective way to discern reality, but as I said, I’m not sure precisely what we’re looking for or how to reliably detect it.

    As the manager receiving a complaint, I take all complaints seriously. What’s at issue is how much objective evidence is required to do something about it. And, in real life, there’s usually no objective evidence at all. It’s all perception.

    A serious response would be greatly appreciated.

  6. says

    You’re asking for a serious response to a deeply unserious question. “Sometimes people say things are racist, but I think sometimes they’re making it up to get sympathy. Can you tell me where to buy a racism detector?”

    You can engage on the issue of understanding racism, you can engage with the idea of listening to people who understand experiences more than you do. Or you can do neither of those things. There is no law against saying racist things (short of things that qualify as hate speech, and even then only in some places). It depends on what your goal is. If your goal is to learn and have more positive interactions, then your demands for “objective evidence” for offense are misguided. If your goal is to sleep soundly at night with the satisfaction that people pointing out racism are just trying to play “the race card”, then you’re free to set whatever standard you like.

  7. Jennifer Allen says

    A manager who works for me hires three new software engineers, all of whom are apparently well qualified and who had all interviewed well. After six months, one of them (the ‘minority’ hire) receives a critical evaluation and is put on the ‘watch list.’ After a year, he/she is recommended for termination. The person in question feels (apparently sincerely) that it’s all or mostly about her/his color/ethnicity/whatever, yet there’s zero objective evidence of any overt discrimination.

    If, in fact, there’s any ‘discrimination’ involved, it’s covert, by which I mean far from obvious.

    At both stages, I’m the reviewing manager. I take the claim of discrimination seriously. In my attempts to verify/refute the alleged discrimination, what do I look for and how do I know if I’ve found it?

    You see, I’m dealing with the real world — which I suspect you are not.

  8. says

    Okay; you’d look for differences in treatment at various conflicts. You’d ask around to see if the non-white dude was getting a harder time for the same results, and ask for specifics of how he’s done roughly equally or better than the white hires, etc.

    You see, I’m dealing with the real world — which I suspect you are not.

    Oh go fuck yourself, honkie. Discrimination is very much a part of the real world, even when it isn’t intentional.

  9. says

    Oh go fuck yourself, honkie

    This kind of approach is really quite unnecessary and unwelcome. Race-based abuse, even if the target is a member of a privileged group, is inappropriate. I’m going to ask you to leave aside this favoured epithet of yours. There’s nothing wrong with reminding people of their privilege, but this approach does not accomplish that.

  10. says

    If you really feel like pretending insults that *nobody* takes seriously are abuse, I’m not going to stop you dude. Can you address the fucking white person who is pretending less-than-obvious racism is all in our heads now.

  11. Jennifer Allen says

    “Can you address the fucking white person who is pretending less-than-obvious racism is all in our heads now.”

    Having followed this thread closely, and having reviewed it again, I’m not aware of anyone who has taken that position.

    Regarding covert discrimination, my own question has consistently been, “I take the claim of discrimination seriously. In my attempts to verify/refute the alleged discrimination, what do I look for and how do I know if I’ve found it?”

    The only response (from the cat who calls me honkee) dealt with overt discrimination and that had already been ruled out.

  12. says

    And, as I already said, the step forward is to engage and listen, not go on some kind of quixotic search for “adequate evidence”. The person says he feels discriminated against – in what way? How has it manifested? What examples can he provide?

  13. Jennifer Allen says

    Crommunist says, “And, as I already said, the step forward is to engage and listen, not go on some kind of quixotic search for ‘adequate evidence’.”

    That’s funny, truly funny. You say that searching for evidence is quixotic, then immediately recommend a (quixotic?) search for evidence.

    “The person says he feels discriminated against – in what way? How has it manifested? What examples can he provide?”

    You’re asking me to search for _overt_ signs of discrimination. I’ve already said there weren’t any. His project leader, his manager, and I (as second level) had all concluded that.

    He didn’t have any concrete examples, says it manifests itself in ‘attitudes’, and says he began to feel it a month to six weeks after he started in the group. He said the feeling was, “I’m not wanted here.” And, he said, that the feeling became more intense as time went by.

    I’ll cut to the chase here to bring this thread to a well deserved end. As it turned out, the guy was absolutely, 100% correct: he wasn’t wanted in the group.

    I learned the truth from a female member of the group, a BS lass of the kind who doesn’t need a Masters or PhD, the kind that just reinvents all that high level learning whenever the task at hand demands it. I’d heard that they’d been dating, but weren’t any more.

    So I took her out and got her semi-smashed. After a couple hours of evasions and euphemisms, she was sufficiently likkered up to give it to me straight.

    “I’m in love with him. He’s a super nice guy, not a mean bone in his body, trustworthy, helpful, and in many ways quite smart. He will be a wonderful husband. But he’s got to find a job he’s capable of handling, a job he can keep. There’s no way I’m gonna get married, or take a chance on getting pregnant, until he does. He’s an OK software engineer who could succeed in a lot of jobs, but in this company and especially in this group, he just can’t cut it. Everyone knows that, except him.

    We offered him a transfer to field service, which he refused as beneath a PhD. And he quit in a huff.

    The guy had been absolutely, 100% correct: he wasn’t wanted in the group. But that, as it turned out, had nothing to do with his race, ethnicity, or whatever.

    About a year later, that BS gal came by my office, waved a ring under my nose, and said, “I’ll be moving to (another city) soon” and traisped off down the hall.

    Happy ending.

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