About Trey Causey

Trey Causey is a PhD candidate in the Department of Sociology at the University of Washington. His dissertation focuses on the role of information in contentious collective action in authoritarian states, using quantitative text analysis of original media data in Arabic and English from Egypt and Jordan in the months before and after the Arab uprisings of 2010-2011. E-mail: trey.causey@gmail.com Web: treycausey.com

R users know it can be finicky in its requirements and opaque in its error messages. The beginning R user often then happily discovers that a mailing list for dealing with R problems with a large and active user base, R-help, has existed since 1997. Then, the beginning R user wades into the waters, asks a question, and is promptly torn to shreds for inadequate knowledge of statistics and/or the software, for wanting to do something silly, or for the gravest sin of violating the posting guidelines. The R user slinks away, tail between legs, and attempts to find another source of help. Or so the conventional wisdom goes. Late last year, someone on Twitter (I don’t remember who, let me know if it was you) asked if R-help was getting meaner. I decided to collect some evidence and find out.

Our findings are surprising, but I think I have some simple sociological explanations.
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Neal Caren of UNC Sociology has put out a call to forecast the number of NRA members in June 2013 using NRA publication subscriber data. Given that Neal’s interests elide with those of several Bad Hessian contributors and per my previous post on predictions in sociology, I took a stab using the forecast package for R. You can find the code I used here and my forecast can be found on scatterplot here.

This is not a post about Nate Silver. I promise. One of the more interesting and well-covered stories of the 2012 US Elections was the so-called “quants vs. pundits” debate that focused–unfairly, given the excellent models developed by Sam Wang, Drew Linzer, and Simon Jackman–on Nate Silver’s Five Thirty Eight forecasting model. I follow a number of social scientists on Twitter and many of their reactions to the success of these models followed along the lines of “YEAH! SCIENCE!” and “+1 for the quants!” and so on. There seemed to be real joy (aside from the fact that many of these individuals were Obama supporters) in the growing public recognition that quantitative forecasting models can produce valid results.
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Working with right-to-left languages like Arabic in R can be a bit of a headache, especially when mixed with left-to-right languages (like English). Since my research involves a great deal of text analysis of Arabic news articles, I find myself with a lot of headaches. Most text analysis methods require some kind of normalization before diving into the actual analyses. Normalization includes things like removing punctuation, converting words to lowercase, stripping numbers out, and so on. This is essential for any kind of frequency-based analysis so that words such as don’t, Don’t, and dont are not considered unique words. After all, when dealing with human-generated text, typos and differences in presentation are bound to occur. Often times, normalizing also includes stemming words so that words such as think, thinking, and thinks are all stemmed to “think” as they all represent (basically) the same concept.

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