Discovering Sociolinguistic Associations with Structured Sparsity
FigsharePublished 1 January 2018Open access
Jacob Eisenstein, Noah A. Smith, Eric P. Xing
Citations124
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TL;DR
A method to discover robust and interpretable sociolinguistic associations from raw geotagged text data is presented, using aggregate demographic statistics about the authors' geographic communities to solve a multi-output regression problem between demographics and lexical frequencies.
Abstract
We present a method to discover robust and interpretable sociolinguistic associations from raw geotagged text data. Using aggregate demographic statistics about the authors' geographic communities, we solve a multi-output regression problem between demographics and lexical frequencies. By imposing a composite ℓ1,∞ regularizer, we obtain structured sparsity, driving entire rows of coefficients to zero. We perform two regression studies. First, we use term frequencies to predict demographic attributes; our method identifies a compact set of words that are strongly associated with author demographics. Next, we conjoin demographic attributes into features, which we use to predict term frequencies. The composite regularizer identifies a small number of features, which correspond to communities of authors united by shared demographic and linguistic properties
Keywords
Social SciencesComputer Science
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