Abstract: Provided are methods and systems for detecting deception in computer-mediated communications that can include (i) parsing digital text to identify processable language-action cues in the digital text, (ii) automatically analyzing, using a computing device and using a machine learning-based classifying algorithm, the processable language-action cues to produce respective probabilities of the digital text including the deceptive content, where the machine learning-based classifying algorithm is trained with training data including (A) training language-action cues from digital data indicating response information known to be deceptive and digital data indicating response information known to be truthful and (B) respective predictor weights associated with the training language-action cues, and (iii) displaying a histogram on a user display device, where the histogram indicates, over a period of time, frequencies of the respective probabilities of the digital text including the deceptive content.

Abstract: Provided are methods and systems for detecting deception in computer-mediated communications that can include (i) parsing digital text to identify processable language-action cues in the digital text, (ii) automatically analyzing, using a computing device and using a machine learning-based classifying algorithm, the processable language-action cues to produce respective probabilities of the digital text including the deceptive content, where the machine learning-based classifying algorithm is trained with training data including (A) training language-action cues from digital data indicating response information known to be deceptive and digital data indicating response information known to be truthful and (B) respective predictor weights associated with the training language-action cues, and (iii) displaying a histogram on a user display device, where the histogram indicates, over a period of time, frequencies of the respective probabilities of the digital text including the deceptive content.