Behavior Research Methods

, Volume 49, Issue 5, pp 1668–1685 | Cite as

Speaking two “Languages” in America: A semantic space analysis of how presidential candidates and their supporters represent abstract political concepts differently



In this article we report a computational semantic analysis of the presidential candidates’ speeches in the two major political parties in the USA. In Study One, we modeled the political semantic spaces as a function of party, candidate, and time of election, and findings revealed patterns of differences in the semantic representation of key political concepts and the changing landscapes in which the presidential candidates align or misalign with their parties in terms of the representation and organization of politically central concepts. Our models further showed that the 2016 US presidential nominees had distinct conceptual representations from those of previous election years, and these patterns did not necessarily align with their respective political parties’ average representation of the key political concepts. In Study Two, structural equation modeling demonstrated that reported political engagement among voters differentially predicted reported likelihoods of voting for Clinton versus Trump in the 2016 presidential election. Study Three indicated that Republicans and Democrats showed distinct, systematic word association patterns for the same concepts/terms, which could be reliably distinguished using machine learning methods. These studies suggest that given an individual’s political beliefs, we can make reliable predictions about how they understand words, and given how an individual understands those same words, we can also predict an individual’s political beliefs. Our study provides a bridge between semantic space models and abstract representations of political concepts on the one hand, and the representations of political concepts and citizens’ voting behavior on the other.


Computational modeling Semantic space Word association Dynamic change Predictive modeling Representation of political concepts 



We would like to thank Jane Hatzell, Ashley Evans, Sha Liu, and Tanner Quiggle for assisting with this research project. We also thank the Institute for CyberScience at Penn State University for the High Performance Computing resources. This work has been supported in part by a University Graduate Fellowship of the Pennsylvania State University. The data and opinions expressed in this article are those of the authors and do not represent the views of the Pennsylvania State University.


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Copyright information

© Psychonomic Society, Inc. 2017

Authors and Affiliations

  1. 1.Department of Psychology and Center for Brain Behavior, and CognitionThe Pennsylvania State UniversityUniversity ParkUSA
  2. 2.Department of Educational Psychology, Counseling, and Special EducationThe Pennsylvania State UniversityUniversity ParkUSA

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