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Measuring and Explaining Political Sophistication through Textual Complexity
Author(s) -
Benoit Kenneth,
Munger Kevin,
Spirling Arthur
Publication year - 2019
Publication title -
american journal of political science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.347
H-Index - 170
eISSN - 1540-5907
pISSN - 0092-5853
DOI - 10.1111/ajps.12423
Subject(s) - sophistication , computer science , pairwise comparison , probabilistic logic , measure (data warehouse) , set (abstract data type) , domain (mathematical analysis) , crowdsourcing , function (biology) , politics , artificial intelligence , natural language processing , data science , data mining , mathematics , political science , world wide web , social science , sociology , mathematical analysis , evolutionary biology , biology , law , programming language
Abstract Political scientists lack domain‐specific measures for the purpose of measuring the sophistication of political communication. We systematically review the shortcomings of existing approaches, before developing a new and better method along with software tools to apply it. We use crowdsourcing to perform thousands of pairwise comparisons of text snippets and incorporate these results into a statistical model of sophistication. This includes previously excluded features such as parts of speech and a measure of word rarity derived from dynamic term frequencies in the Google Books data set. Our technique not only shows which features are appropriate to the political domain and how, but also provides a measure easily applied and rescaled to political texts in a way that facilitates probabilistic comparisons. We reanalyze the State of the Union corpus to demonstrate how conclusions differ when using our improved approach, including the ability to compare complexity as a function of covariates.