  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.
