Veritaps: Truth estimation from mobile interaction

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Standard

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 proceedingArticle in proceedingsResearchpeer-review

Harvard

Mottelson, A, Knibbe, J & Hornbæk, K 2018, Veritaps: Truth estimation from mobile interaction. in CHI 2018 - Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems: Engage with CHI., 561, Association for Computing Machinery, 2018 CHI Conference on Human Factors in Computing Systems, CHI 2018, Montreal, Canada, 21/04/2018. https://doi.org/10.1145/3173574.3174135

APA

Mottelson, A., Knibbe, J., & Hornbæk, K. (2018). Veritaps: Truth estimation from mobile interaction. In CHI 2018 - Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems: Engage with CHI [561] Association for Computing Machinery. https://doi.org/10.1145/3173574.3174135

Vancouver

Mottelson A, Knibbe J, Hornbæk K. Veritaps: Truth estimation from mobile interaction. In CHI 2018 - Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems: Engage with CHI. Association for Computing Machinery. 2018. 561 https://doi.org/10.1145/3173574.3174135

Author

Mottelson, Aske ; Knibbe, Jarrod ; Hornbæk, Kasper. / Veritaps : Truth estimation from mobile interaction. CHI 2018 - Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems: Engage with CHI. Association for Computing Machinery, 2018.

Bibtex

@inproceedings{4e737ce793f14d61ad6c36b37f6d972c,
title = "Veritaps: Truth estimation from mobile interaction",
abstract = "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.",
keywords = "Deception, Dishonesty, Lie detection, Mobile input, Polygraph, Smartphones",
author = "Aske Mottelson and Jarrod Knibbe and Kasper Hornb{\ae}k",
year = "2018",
doi = "10.1145/3173574.3174135",
language = "English",
booktitle = "CHI 2018 - Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems",
publisher = "Association for Computing Machinery",

}

RIS

TY - GEN

T1 - Veritaps

T2 - Truth estimation from mobile interaction

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

BT - CHI 2018 - Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems

PB - Association for Computing Machinery

ER -

ID: 203773782