April 02, 2014

New Book Chapter on the Geographies of Beer on Twitter

We're pleased to announce a new publication by members of the Floatingsheep team. Just released is "Offline Brews and Online Views: Exploring the Geography of Beer on Twitter", a new book chapter written by Matt and Ate that analyzes the geographies of beer-related tweeting activity. Published in a new edited collection from Springer appropriately- and straightforwardly-entitled The Geography of Beer, Matt and Ate's paper -- the latest in Floatingsheep's long line of investigations into the geographies of beer -- shows that geotagged tweets about beer, and other alcoholic beverages for that matter, are reflective of people's offline consumption preferences.

Using a database of one million geotagged tweets from June 2012 to May 2013 containing the keywords "wine", "beer" or the names of a range of light or cheaper beers within the continental US, some clear regional variations in alcoholic beverage preference are detected. For instance, when comparing tweets referencing "wine" to those referencing "beer", wine-related tweets tend to be more dominant along both the east and west coasts of the US. But this kind of variation is present even when comparing different brands of light beer. While Bud Light is more popular in the eastern and southeastern US, Coors Light tends to dominate the west coast, with Miller Lite and Busch Light being preferred in the midwest and Great Plains. The dominance of these brands in virtual space is no surprise, as they also dwarf the competition in actual sales.

But these regional variations are even more distinct when one looks at locally- or regionally-specific brands. While some of these cheaper (which is not to say less delicious!) beers have reached a national or even international market, others remain popular in only a very limited region, owing either to local tradition or simply limited distribution outside of their home-markets. Nonetheless, by mapping the concentrations of geotagged tweets referencing each of these brands, we're able to uncover these regional particularities, as is shown in the map below, taken from Matt and Ate's chapter.

Aggregated Geographies of Tweets referencing Regional 'Cheap' Beers

From Sam Adams in New England to Yuengling in Pennsylvania to Grain Belt and Schlitz in the upper Midwest, these beers are quite clearly associated with particular places. Other beers, like Hudepohl and Goose Island are interesting in that they stretch out from their places of origin -- Cincinnati and Chicago, respectively -- to encompass a much broader region where there tend to be fewer regionally-specific competitors, at least historically. On the other hand, beers like Lone Star, Corona and Dos Equis tend to have significant overlap in their regional preferences, with all three having some level of dominance along the US-Mexico border region, but with major competition between these brands in both Arizona and Texas.

Beer, like many other social practices, may be millennia-old, but the socio-spatial practices associated with it – checking into a brewery, posting a review, geotagging a photo – continue to evolve with technological change. As such, this kind of data provides an important way to capture these socio-spatial practices and preferences, while demonstrating how even in an era of supposed globalization and homogenization, regional histories and cultures continue to be reflected online in important ways.

If you don't have access and would like to read more about this, please contact Matt at zook [at] uky [dot] edu for a pre-publication version of the chapter. Bottoms up!

The full citation for Matt and Ate's chapter is below:
Zook, M. and A. Poorthuis. 2014. "Offline Brews and Online Views: Exploring the Geography of Beer Tweets". In The Geography of Beer, eds. M. Patterson and N. Hoalst-Pullen. Springer. pp. 201-209.

1 comment:

  1. Hello I am a GIS student at the University of Oklahoma, I am thoroughly curious how you were able to get a hold of the data for these projection? Twitter would be a great data source, but I can't figure out how you get the data in the first place. If you have the time I'd love to have access to this specific data set for a project I am doing for class.


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