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Language use reveals information about who we are and how we feel1-3. One ofthe pioneers in text analysis, Walter Weintraub, manually counted which typesof words people used in medical interviews and showed that the frequency offirst-person singular pronouns (i.e., I, me, my) was a reliable indicator ofdepression, with depressed people using I more often than people who are notdepressed4. Several studies have demonstrated that language use also differsbetween truthful and deceptive statements5-7, but not all differences areconsistent across people and contexts, making prediction difficult8. Here weshow how well linguistic deception detection performs at the individual levelby developing a model tailored to a single individual: the current USpresident. Using tweets fact-checked by an independent third party (WashingtonPost), we found substantial linguistic differences between factually correctand incorrect tweets and developed a quantitative model based on thesedifferences. Next, we predicted whether out-of-sample tweets were eitherfactually correct or incorrect and achieved a 73% overall accuracy. Our resultsdemonstrate the power of linguistic analysis in real-world deception researchwhen applied at the individual level and provide evidence that factuallyincorrect tweets are not random mistakes of the sender.

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