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6 results on '"Havrileck, Alice"'

1. A personal model of trumpery: Deception detection in a real-world high-stakes setting

Author

van der Zee, Sophie, Poppe, Ronald, Havrileck, Alice, and Baillon, Aurelien

Subjects
Computer Science - Computation and Language, Computer Science - Human-Computer Interaction
Abstract

Language use reveals information about who we are and how we feel1-3. One of the pioneers in text analysis, Walter Weintraub, manually counted which types of words people used in medical interviews and showed that the frequency of first-person singular pronouns (i.e., I, me, my) was a reliable indicator of depression, with depressed people using I more often than people who are not depressed4. Several studies have demonstrated that language use also differs between truthful and deceptive statements5-7, but not all differences are consistent across people and contexts, making prediction difficult8. Here we show how well linguistic deception detection performs at the individual level by developing a model tailored to a single individual: the current US president. Using tweets fact-checked by an independent third party (Washington Post), we found substantial linguistic differences between factually correct and incorrect tweets and developed a quantitative model based on these differences. Next, we predicted whether out-of-sample tweets were either factually correct or incorrect and achieved a 73% overall accuracy. Our results demonstrate the power of linguistic analysis in real-world deception research when applied at the individual level and provide evidence that factually incorrect tweets are not random mistakes of the sender.

Published
2018

3. A Personal Model of Trumpery: Linguistic Deception Detection in a Real-World High-Stakes Setting

Author

Van Der Zee, Sophie, Poppe, Ronald, Havrileck, Alice, Baillon, Aurélien, Sub Social and Affective Computing, Social and Affective Computing, Sub Social and Affective Computing, Social and Affective Computing, Applied Economics, Groupe d'Analyse et de Théorie Economique Lyon - Saint-Etienne (GATE Lyon Saint-Étienne), École normale supérieure de Lyon (ENS de Lyon)-Université Lumière - Lyon 2 (UL2)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Université Jean Monnet - Saint-Étienne (UJM)-Centre National de la Recherche Scientifique (CNRS), and emlyon business school (EM)

Subjects
Deception, 05 social sciences, Twitter, LIWC, [SHS.PSY]Humanities and Social Sciences/Psychology, open data, 050109 social psychology, Linguistics, deception detection, 050105 experimental psychology, open materials, [SHS]Humanities and Social Sciences, tailored model, Humans, 0501 psychology and cognitive sciences, Psychology(all), General Psychology, linguistic analysis
Abstract

International audience; 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.

Published
2021

4. sj-docx-1-pss-10.1177_09567976211015941 ��� Supplemental material for A Personal Model of Trumpery: Linguistic Deception Detection in a Real-World High-Stakes Setting

Author

Van Der Zee, Sophie, Poppe, Ronald, Havrileck, Alice, and Baillon, Aur��lien

Subjects
FOS: Psychology, FOS: Clinical medicine, 170199 Psychology not elsewhere classified, 110319 Psychiatry (incl. Psychotherapy), 110904 Neurology and Neuromuscular Diseases, Neuroscience
Abstract

Supplemental material, sj-docx-1-pss-10.1177_09567976211015941 for A Personal Model of Trumpery: Linguistic Deception Detection in a Real-World High-Stakes Setting by Sophie Van Der Zee, Ronald Poppe, Alice Havrileck and Aur��lien Baillon in Psychological Science

Published
2021
Full Text
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5. A Personal Model of Trumpery:Linguistic Deception Detection in a Real-World High-Stakes Setting

Author

Van Der Zee, Sophie, Poppe, Ronald, Havrileck, Alice, Baillon, Aurélien, Van Der Zee, Sophie, Poppe, Ronald, Havrileck, Alice, and Baillon, Aurélien

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.

Published
2021

6. A Personal Model of Trumpery: Linguistic Deception Detection in a Real-World High-Stakes Setting.

Author

Van Der Zee, Sophie, Poppe, Ronald, Havrileck, Alice, and Baillon, Aurélien

Subjects
*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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