Deceit detection via online behavioral learning

Bhaskaran, Nisha; Nwogu, Ifeoma; Frank, Mark G.; Govindaraju, Venu

doi:10.1145/1982185.1982193

Cited by 2 publications

(1 citation statement)

References 4 publications

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“…Table one provides examples of both face-to-face and linguistic cues as determined by DePaulo, et al [12]. For example [14] presented a framework for detecting deceit using a dynamic Bayesian model of eye movement that achieved 82% classification accuracy. Our work concentrates solely on linguistic cues, with the goal of understanding how the increasing reliance on new communication media may provide similar indicators of deception.…”

Section: Cues To Deception In Communicationmentioning

confidence: 99%

Cues to Deception in Social Media Communications

Briscoe

Appling

Hayes

2014

2014 47th Hawaii International Conference on System Sciences

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With the increasing reliance on social media as a dominant communication medium for current news and personal communications, communicators are capable of executing deception with relative ease. While past-related research has investigated written deception in traditional forms of computer mediated communication (e.g. email), we are interested determining if those same indicators hold in social media-like communication and if new, social-media specific linguistic cues to deception exist. Our contribution is two-fold: 1) we present results on human subjects experimentation to confirm existing and new linguistic cues to deception; 2) we present results on classifying deception from training machine learning classifiers using our best features to achieve an average 90% accuracy in cross fold validation.

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Section: Cues To Deception In Communicationmentioning

confidence: 99%