Bond Gary D, Speller Lassiter F, Cockrell Lauren L, Webb Katelynn G, Sievers Jaci L
Department of Psychology, Eastern New Mexico University, Portales, NM, USA.
Psychol Rep. 2023 Dec;126(6):3090-3103. doi: 10.1177/00332941221105212. Epub 2022 May 28.
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.
2020年美国总统大选是一场堪称“近代史上最恶劣的总统竞选之一”的活动，部分原因是大选辩论极具争议性，频繁出现打断情况，候选人之间还多次相互辱骂和恶语相向。本研究使用语言调查与字数统计软件（LIWC）中的现实监控（RM）欺骗检测算法，将辩论中使用的语言与经过事实核查的真相和谎言进行比较，以调查政治背景下现实生活中高风险口头信息的真实性。我们发现，总体而言，RM分数较低，辩论语言与经过事实核查的谎言之间没有显著差异，而在经过事实核查的真实陈述中，RM分数显著更高，这表明大多数辩论语言具有欺骗性。这一结果支持了LIWC中的RM算法在政治背景下能够区分真相与谎言以及辩论语言的这一发现。本研究中60.7%的分类准确率可能反映了经过事实核查的谎言和真实陈述字数相对较少的问题，但很可能反映了个别候选人陈述中使用的RM特征存在偏差。每个人在交流中都有自己的风格——“人们说话和写作的方式已被视为个人身份的标志”。即使有来自同一位候选人的大量陈述语料库，他们在真实和欺骗性信息中可能也会经常放大RM的某些特征，而减少RM的其他特征。这是一个富有成果的研究领域，未来的研究可以对此进行探索。
