@article{ref1,
title="A personal model of Trumpery: linguistic deception detection in a real-world high-stakes setting",
journal="Psychological science",
year="2021",
author="Van Der Zee, Sophie and Poppe, Ronald and Havrileck, Alice and Baillon, Aurélien",
volume="ePub",
number="ePub",
pages="ePub-ePub",
abstract="Language use differs between truthful and deceptive statements, but not all differences are consistent across people and contexts, complicating the identification of deceit in individuals. By relying on fact-checked tweets, we showed in three studies (Study 1: 469 tweets; Study 2: 484 tweets; Study 3: 24 models) how well personalized linguistic deception detection performs by developing the first deception model tailored to an individual: the 45th U.S. president. First, we found substantial linguistic differences between factually correct and factually incorrect tweets. We developed a quantitative model and achieved 73% overall accuracy. Second, we tested out-of-sample prediction and achieved 74% overall accuracy. Third, we compared our personalized model with linguistic models previously reported in the literature. Our model outperformed existing models by 5 percentage points, demonstrating the added value of personalized linguistic analysis in real-world settings. Our results indicate that factually incorrect tweets by the U.S. president are not random mistakes of the sender.<p /> <p>Language: en</p>",
language="en",
issn="0956-7976",
doi="10.1177/09567976211015941",
url="http://dx.doi.org/10.1177/09567976211015941"
}