What about the sample size? 395 doesn’t seem like that many?
The 395 tweets mentioned are the number of geocoded tweets referencing the given keywords from November 1 until November 7 at approximately 4:00 pm EST. This is NOT a sample, but the total population of geocoded tweets that matched our search criteria as outlined in the post. Geocoded tweets make up a tiny fraction of overall Twitter activity (could be as large as 5% or as small as less than 1%), so the actual number of tweets referencing these keywords is likely much, much larger, though we are not sure as to this number.
That said, we don't know what the geographical distribution of non-geocoded tweets is. However, given that many geocoded tweets are the product of GPS-enabled smart phones, it is likely that geocoded tweets tend to come from wealthier locations. All things being equal, this means that the geocoded data likely underrepresents relatively poorer and more rural locations. Should this actually be the case, the location quotients for Mississippi and Alabama would actually be even higher than our initial study showed, but the exact nature of this phenomena is unknown.
Note: People concerned about our methodology should also check out our post on 11/12/2012 using geocoded tweets to located the epicenter of an earthquake in Kentucky. (this paragraph added at 10:20 am EST 11/12/2012)
Why didn’t you map references to hateful comments towards Mitt Romney?
First, the motivation for this posting was the observations posted on the Jezebel blog linked in our original post, noting the uptick in racist tweets following President Obama’s re-election. Second, we focus on racist language directed at President Obama because racism directed at black Americans is not only historically more significant, but because it also highlights the persistence of explicitly racist attitudes in what some have (fallaciously) termed ‘post-racial America’. Third, we did check for both the number of tweets referencing Mitt Romney containing some racially charged terms, as well as the number of derogatory comments about white people. Depending on the terminology used, the results show that there are 7-15x the amount hateful tweets direct towards President Obama than Mitt Romney.
Finally, if this is your first response to our map, and not “that’s really f---ed up!”, then we probably have more important issues to deal with than the minutiae of our methodology. Though we endorse neither hatred, discrimination or violence against anyone, we refuse to acknowledge the equivalence of the terms being used to describe President Obama and Mitt Romney.
Did you remove uses of the “N word” that were positive?
No. We didn’t filter the tweets used in this database, however a quick look at the data reveals that most are derogatory in nature. By leaving the data as is, we are more easily able to compare the number of references to, say, the kinds of comments about Mitt Romney people are clamoring for us to map, without inserting ourselves into an undoubtedly subjective filtering process. Regardless, even if we were to filter tweets, it very well might not change the overall spatial distribution, e.g., a filtered tweet could be from California or Alabama, leaving the map looking essentially the same as it currently does.
A further point is that the term ‘n----r’ is almost universally associated with negative, derogatory intent, as opposed to the more colloquialized (and appropriated by the black community) ‘n---a’, which a quick inspection of the data shows is used more positively. References to ‘n---a’ were not included in the study.
What about multiple tweets by the same individual?
Like our decision not to filter tweets based on their context, nor did we filter based on multiple tweets by the same individual. However, a quick look at the map indicates that tweeting activity is not entirely concentrated at any individual point, meaning that barring the remote possibility of a hyper-mobile tweeter fixated on racist slurs or a racist twitter bot, this is not issue enough to undermine our findings.
Moreover, when we returned to the data and looked at users rather than tweets, very little changes in the location quotients, with Alabama’s being even higher. We thus see this as being a moot point.
Are you saying I’m racist because I didn’t vote for Obama? Are you saying that everyone in a state that had more racist tweets is racist?
No and no. Nor do we imply such a thing anywhere in our original posts or our reactions to comments. However, we believe that the concentration of racist tweets in the South is indicative of the persistence of racism in the South, which is correlated with, though not necessarily causally-related to, statewide voting for Mitt Romney. Just because you live in Mississippi or Alabama does not make you a terrible person. If, however, you use the “N word” to degrade an individual or group of people, as the tweets we are talking about here do, it’s a different story altogether.
What else do you have to say for yourself?
This map and blog post have received more attention than we could have imagined, most of it positive and thought-provoking. Though racism undoubtedly remains a touchy subject, and one perhaps not best dealt with by fairly simple maps, we hoped to use this exercise to show the persistence of racism in the US, even with the country’s first black president being re-elected to a second term, and the need to address this head on, rather than counter such explicitly racist language and behavior with claims of ‘reverse racism’ as many of the critics of our map have done.
Of course, our map does not encompass the entirety of racism as it is experienced by black Americans, much less members of other groups who are systemically discriminated against, both in explicit language directed at these individuals and groups, as well as structural forms of racism that continually limit the ability of people to live happy, healthy and comfortable lives. As geographers, we like to think of ourselves as especially attuned to such issues. However, as the focus of this blog is dedicated to studying the world through the lens of the geoweb, we limit ourselves in this forum to analyses like those presented in the original post.
Thanks for clarifying. Most of those I could have guessed the answer so it does make sense.
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