Publication Type
Journal Article
Version
publishedVersion
Publication Date
4-2019
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.
Discipline
Models and Methods | Political Science
Research Areas
Political Science
Publication
American Journal of Political Science
Volume
63
Issue
2
First Page
491
Last Page
508
ISSN
0092-5853
Identifier
10.1111/ajps.12423
Publisher
Wiley
Citation
BENOIT, Kenneth, MUNGER, Kevin, & SPIRLING, Arthur.(2019). Measuring and explaining political sophistication through textual complexity. American Journal of Political Science, 63(2), 491-508.
Available at: https://ink.library.smu.edu.sg/soss_research/3974
Copyright Owner and License
Authors-CC-BY-NC
Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.
Additional URL
https://doi.org/10.1111/ajps.12423