I thought some of you would find this enlightening/amusing:
Predicting the political alignment of twitter users. 2011. Michael D.
Conover, Bruno Gonc¸alves, Jacob Ratkiewicz, Alessandro Flammini and
Filippo Menczer, Center for Complex Networks and Systems Research,
School of Informatics and Computing
Indiana University
http://cnets.indiana.edu/wp-content/uploads/conover_prediction_socialcom_pdfexpress_ok_version.pdf"Abstract—The widespread adoption of social media for political
communication creates unprecedented opportunities to
monitor the opinions of large numbers of politically active
individuals in real time. However, without a way to distinguish
between users of opposing political alignments, conflicting signals
at the individual level may, in the aggregate, obscure partisan
differences in opinion that are important to political strategy.
In this article we describe several methods for predicting the
political alignment of Twitter users based on the content and
structure of their political communication in the run-up to the
2010 U.S. midterm elections. Using a data set of 1,000 manually annotated
individuals, we find that a support vector machine
(SVM) trained on hashtag metadata outperforms an SVM trained
on the full text of users’ tweets, yielding predictions of political
affiliations with 91% accuracy. Applying latent semantic analysis
to the content of users’ tweets we identify hidden structure in the
data strongly associated with political affiliation, but do not find
that topic detection improves prediction performance. All of these
content-based methods are outperformed by a classifier based
on the segregated community structure of political information
diffusion networks (95% accuracy). We conclude with a practical
application of this machinery to web-based political advertising,
and outline several approaches to public opinion monitoring
based on the techniques developed herein."
BTW, text analysis, SVM and latent semantic analysis are my research areas...
-Glen Newton
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http://zzzoot.blogspot.com/-
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