I don’t know annoyed, using their algorithm the most swearing goes on in the South culminating at the Louisiana/Texas border. Being from Louisiana but having lived in New York and now residing in Los Angeles, I totally believe that

Not sure how this made it on here, but, in answer to the comments above — if you read through the part of the map in the lower left corner which describes the analysis done, you will realize that this is not merely a population map. This is a map of “% of words in tweets which are profanities.” Number of tweets in an area (a proxy for population) doesn’t enter into it. If an area lights up, it’s because, in my limited sample, people there were more likely to swear, not that more people total were swearing. I specifically worked to ensure that this was not simply a population density map. Please understand before criticizing based on your assumptions.

My guess is that ppl who would use twitter in a business setting or would possibly have their tweets read by a boss, etc. might clean up the language. Those places would be the higher population centers.

I love this map Daniel–I am glad to see it making the rounds and generating some discussion. Keep doing interesting work!

For those of you interested in Daniel’s work, I want to mention that Daniel also penned an article on the cartographic technique of waterlining for the recently released special digital issue of Cartographic Perspectives (#66); his Twitter Profanity map was featured on the cover of the special issue. You can download the full issue here (open access): http://bit.ly/gYJ0yK

Where can we find the list of profanities used? For example, there are words permissible on T.V. that I would consider profanity, such as b###h, c##p, and a##. I would also like to know if racial slurs are included.

I’m from Louisiana, too … I think the “hot spot” is just Texans and Louisianians trying to communicate with each other, that’s all!

Not sure how you arrived at the conclusion that the plains are more polite than the mountains. Seems to me that the opposite is true when compared to a relief map. Maybe you should try using digital elevation data and see if there’s a correlation.

@P — The title is meant to suggest the 3d swearing surface, where a lot of swearing would generate a statistical mountain of data, and no swearing would be a flat lowland. It appears, though, that I did not consider other peoples’ potential interpretations of the title based on actual physical geography. Live and learn.

@mdc2141 — For more information on the words used (somewhat arbitrarily chosen), look here, especially at the comments: http://cartastrophe.wordpress.com/2011/01/24/no-swearing-in-utah/

need a bright white spot in north jersey with another near the jersey shore XD

Next time try using multiple colors instead of shades of red.

I fully understand the map.

It’s interesting how southern Indiana, Kentucky, and eastern Tennessee make up a virtual “nice” valley surrounded by mountains of profanity.

Wondering what a map with the ethnicity of Tweeter taken into account would look like when laid over this map. Not trying to infer anything about any particular race, just wondering.

For those of you who apparently are not reading Daniel Huffman’s comments, not really reading the map correctly and thus jumping to wrong conclusions, here’s the fine print, taken directly from the map itself:

Profane Mountains, Polite Plains
Profanity on Twitter, March/April 2010
Isolines are based upon an interpolated surface generated from approximately 1.5 million geocoded public posts on Twitter between March 9th and April 12th, 2010. These data represent only a sample of all posts made during that period. Isolines are based upon the average number of profanities found in the 500 nearest data points, in order to compensate for low population areas.

In other words, his data represent the percentage of tweets by geographical area that contain profanities. It is not measuring population or rural vs. urban. In fact, the exercise is designed to exclude those factors. The correlation between use of profanities and urban centers is something the visualization simply makes apparent.

amusing comments. rife with misguided generalizations about the swearing habits of a given region, yet predicating such conclusions on the assumption that the entire american populace uses twitter and agrees on what words constitute profanity.