05-19-2013, 02:48 PM
I noticed if you zoom in as far as you can that the maps show odd really "hot" spots in rural areas. In an effort to track down why that might be the case I found this:
http://www.floatingsheep.org/2013/05/hatemap.html
The quad cities isn't a highly populated area, but there are probably at least a few thousand twitter users in that area. Yet, they found only 31 unique Twitter users who used the "N" word in a hateful way. It's quite possible that they belong to inter-related groups and once one person broke the norm of not using the word on Twitter, then like-minded people in the inter-related groups felt okay about doing the same. It seems quite possible to me that in such a situation, if one person hadn't started it, then that area may not have shown up at all. If you have the same dynamic taking place in a large metropolitan area where you get a cluster of 30 people or so belonging to inter-related groups firing off twitters with the "N" word, that cluster may not show up in the metropolitan area at all because of the normalization of the data - even though it would involve exactly the same number of people in similar inter-related groups. Perhaps that isn't the explanation, but it does seem plausible.
At any rate, the authors of the maps say that there is little meaningful spatial distribution conclusions that one can draw from the data. They should have put that disclaimer front and center on the map.
http://www.floatingsheep.org/2013/05/hatemap.html
Even when normalized, many of the slurs included in our analysis display little meaningful spatial distribution. For example, tweets referencing ‘nigger’ [N word] 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.
The quad cities isn't a highly populated area, but there are probably at least a few thousand twitter users in that area. Yet, they found only 31 unique Twitter users who used the "N" word in a hateful way. It's quite possible that they belong to inter-related groups and once one person broke the norm of not using the word on Twitter, then like-minded people in the inter-related groups felt okay about doing the same. It seems quite possible to me that in such a situation, if one person hadn't started it, then that area may not have shown up at all. If you have the same dynamic taking place in a large metropolitan area where you get a cluster of 30 people or so belonging to inter-related groups firing off twitters with the "N" word, that cluster may not show up in the metropolitan area at all because of the normalization of the data - even though it would involve exactly the same number of people in similar inter-related groups. Perhaps that isn't the explanation, but it does seem plausible.
At any rate, the authors of the maps say that there is little meaningful spatial distribution conclusions that one can draw from the data. They should have put that disclaimer front and center on the map.