The 2020 U.S. Presidential election was a campaign that could be characterized as 'one of the nastiest presidential campaigns in recent memory,' partly because the general election debates were highly contentious and featured frequent interruptions and several insults and invectives between candidates. This research compared the language used in the debates to fact-checked truths and lies using a Reality Monitoring (RM) deception detection algorithm in Linguistic Inquiry and Word Count (LIWC) to investigate the veracity of real-life high-stakes verbal messages in the political context. We found that overall RM scores were lower and not significantly different between debate language and fact-checked lies, and RM scores were significantly higher in fact-checked truth statements, indicating that most debate language uttered was deceptive. This result supports the finding that the RM algorithm in LIWC distinguishes truth from lies and debate language in the context of politics. The 60.7% classification rate in this study may reflect a problem with the relatively short word counts of fact-checked lie and truth statements, but most probably reflects individual candidates' deviations in RM features used in their statements. Each individual has a style that they use in communication-'the way people talk and write have been recognized as stamps of individual identity.' Even with a corpus of many statements from the same individual candidates, they probably regularly amplify certain features of RM and diminish other features of RM in their truthful and deceptive messages. This is a fruitful area of research that could be explored in future studies.
Keywords: Linguistic inquiry and word count; deception detection; politics; reality monitoring.