Mapping moral language on US presidential primary campaigns reveals rhetorical networks of political division and unity

Kobi Hackenburg*, William J. Brady, Manos Tsakiris

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

During political campaigns, candidates use rhetoric to advance competing visions and assessments of their country. Research reveals that the moral language used in this rhetoric can significantly influence citizens' political attitudes and behaviors; however, the moral language actually used in the rhetoric of elites during political campaigns remains understudied. Using a data set of every tweet (N = 139, 412) published by 39 US presidential candidates during the 2016 and 2020 primary elections, we extracted moral language and constructed network models illustrating how candidates' rhetoric is semantically connected. These network models yielded two key discoveries. First, we find that party affiliation clusters can be reconstructed solely based on the moral words used in candidates' rhetoric. Within each party, popular moral values are expressed in highly similar ways, with Democrats emphasizing careful and just treatment of individuals and Republicans emphasizing in-group loyalty and respect for social hierarchies. Second, we illustrate the ways in which outsider candidates like Donald Trump can separate themselves during primaries by using moral rhetoric that differs from their parties' common language. Our findings demonstrate the functional use of strategic moral rhetoric in a campaign context and show that unique methods of text network analysis are broadly applicable to the study of campaigns and social movements.

Original languageEnglish (US)
Article numberpgad189
JournalPNAS Nexus
Volume2
Issue number6
DOIs
StatePublished - Jun 1 2023

Keywords

  • moral foundations theory
  • moral language
  • natural language processing
  • network analysis
  • political campaigns

ASJC Scopus subject areas

  • General

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