1. A Personal Model of Trumpery: Linguistic Deception Detection in a Real-World High-Stakes Setting.
- Author
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Van Der Zee, Sophie, Poppe, Ronald, Havrileck, Alice, and Baillon, Aurélien
- Subjects
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DECEPTION , *LINGUISTIC analysis , *MICROBLOGS , *LINGUISTIC models - 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. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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