If you were lisitening to NPR’s “All Things Considered” broadcast on January 18, you might have heard a brief report on research that reveals regional differences (“dialects”) in word usage, spellings, slang and abbreviations in Twitter postings. For example, Northern and Southern California use spelling variants koo and coo to mean “cool.”
Finding regional differences in these written expressions is interesting in its own right, but I’ve just finished reading the paper describing this research and there’s a lot more going on here than simply counting and comparing expressions across different geographic regions. The paper is an excellent example of what market researchers might do to analyze social media.
The study authors–Jacob Eisenstein, Brendan O’Connor, Noah A. Smith, and Eric P. Xing–are affiliated with the School of Computer Science at Carnegie Mellon University (Eisenstein, who was interviewed for the ATC broadcast, is a postdoctoral fellow). They set out to develop a latent variable model to predict an author’s geographic location from the characteristics of text messages. As they point out, there work is unique in that they use raw text data (although “tokenized”) as input to the modeling. They develop and compare a few different models, including a “geographic topic model” that incorporates the interaction between base topics (such as sports) and an author’s geographic location as well as additional latent variable models: a “mixture of unigrams” (model assumes a single topic) and a “supervised linear Dirichlet allocation.” If you have not yet figured it out, the models, as described, use statistical machine learning methods. That means that some of the terminology may be unfamiliar to market researchers, but the description of the algorithm for the geographic topic model resembles the hierarchical Bayesian methods using the Gibb’s sampler that have come into fairly wide use in market research (especially for choice-based conjoint analysis).
This research is important for market research because it demonstrates a method for estimating characteristics of individual authors from the characteristics of their social media postings. While we have not exhausted the potential of simpler methods (frequency and sentiment analyses, for example), this looks like the future of social media analysis for marketing.
Copyright 2011 by David G. Bakken. All rights reserved.