Language in the high-stakes 2016 US presidential primary campaign was contentious, filled with name-calling, personal attacks, and insults. Language in debates served at least three political functions: for image making, to imagine potential realities currently not in practice, and to disavow facts. In past research, the reality monitoring (RM) framework has discriminated accurately between truthful and deceptive accounts (similar to 70% classification). Truthful accounts show greater sensory, time and space, and affective information, with little evidence of cognitive operations. An RM algorithm was used with Linguistic Inquiry and Word Count software to code candidates' language. RM scores were significantly higher in fact-checked truth statements than in lies, and debate language in the 2016 primaries was as deceptive as fact-checked lies. In a binary logistic regression model, one RM criterion, cognitive processes, predicted veracity using computerized RM, classifying 87% of fact-checked truth statements but only 28% of fact-checked lie statements (63% classification overall).Copyright (c) 2017 John Wiley & Sons, Ltd.
