Veritaps: Truth estimation from mobile interaction
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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Veritaps : Truth estimation from mobile interaction. / Mottelson, Aske; Knibbe, Jarrod; Hornbæk, Kasper.
CHI 2018 - Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems: Engage with CHI. Association for Computing Machinery, 2018. 561.Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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TY - GEN
T1 - Veritaps
T2 - 2018 CHI Conference on Human Factors in Computing Systems, CHI 2018
AU - Mottelson, Aske
AU - Knibbe, Jarrod
AU - Hornbæk, Kasper
PY - 2018
Y1 - 2018
N2 - We introduce the concept of Veritaps: a communication layer to help users identify truths and lies in mobile input. Existing lie detection research typically uses features not suitable for the breadth of mobile interaction. We explore the feasibility of detecting lies across all mobile touch interaction using sensor data from commodity smartphones. We report on three studies in which we collect discrete, truth-labelled mobile input using swipes and taps. The studies demonstrate the potential of using mobile interaction as a truth estimator by employing features such as touch pressure and the inter-tap details of number entry, for example. In our final study, we report an F1-score of:98 for classifying truths and:57 for lies. Finally we sketch three potential future scenarios of using lie detection in mobile applications; as a security measure during online log-in, a trust layer during online sale negotiations, and a tool for exploring self-deception.
AB - We introduce the concept of Veritaps: a communication layer to help users identify truths and lies in mobile input. Existing lie detection research typically uses features not suitable for the breadth of mobile interaction. We explore the feasibility of detecting lies across all mobile touch interaction using sensor data from commodity smartphones. We report on three studies in which we collect discrete, truth-labelled mobile input using swipes and taps. The studies demonstrate the potential of using mobile interaction as a truth estimator by employing features such as touch pressure and the inter-tap details of number entry, for example. In our final study, we report an F1-score of:98 for classifying truths and:57 for lies. Finally we sketch three potential future scenarios of using lie detection in mobile applications; as a security measure during online log-in, a trust layer during online sale negotiations, and a tool for exploring self-deception.
KW - Deception
KW - Dishonesty
KW - Lie detection
KW - Mobile input
KW - Polygraph
KW - Smartphones
UR - http://www.scopus.com/inward/record.url?scp=85046951454&partnerID=8YFLogxK
U2 - 10.1145/3173574.3174135
DO - 10.1145/3173574.3174135
M3 - Article in proceedings
AN - SCOPUS:85046951454
BT - CHI 2018 - Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems
PB - Association for Computing Machinery
Y2 - 21 April 2018 through 26 April 2018
ER -
ID: 203773782