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MUMBAI: multi-person, multimodal board game affect and interaction analysis dataset [<Journal>]
Doyran, Metehan [Verfasser]; Schimmel, Arjan [Verfasser]; Baki, Pınar [Verfasser].
DNB Subject Category Language
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2
A Personal Model of Trumpery: Linguistic Deception Detection in a Real-World High-Stakes Setting ...
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3
A Personal Model of Trumpery: Linguistic Deception Detection in a Real-World High-Stakes Setting ...
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4
Datasets and code of the paper: 'A personal model of trumpery: Linguistic deception detection in a real-world high-stakes setting' ...
van der Zee, Sophie; Baillon, Aurelien; Poppe, Ronald. - : Erasmus University Rotterdam (EUR), 2021
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5
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 ...
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6
Datasets and code of the paper: 'A personal model of trumpery: Linguistic deception detection in a real-world high-stakes setting' ...
van der Zee, Sophie; Baillon, Aurelien; Poppe, Ronald. - : Erasmus University Rotterdam (EUR), 2021
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7
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 ...
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8
Multitask Learning to Improve Egocentric Action Recognition ...
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9
A personal model of trumpery: Deception detection in a real-world high-stakes setting ...
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 ...
Keyword: Computation and Language cs.CL; FOS Computer and information sciences; Human-Computer Interaction cs.HC
URL: https://arxiv.org/abs/1811.01938
https://dx.doi.org/10.48550/arxiv.1811.01938
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10
Virtual meeting rooms: from observation to simulation
In: AI & society. - Guildford : Springer-Verl. London 22 (2008) 2, 133-144
OLC Linguistik
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