{"title":"Veritaps:来自移动交互的真实估计","authors":"Aske Mottelson, Jarrod Knibbe, K. Hornbæk","doi":"10.1145/3173574.3174135","DOIUrl":null,"url":null,"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.","PeriodicalId":20512,"journal":{"name":"Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems","volume":"19 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2018-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Veritaps: Truth Estimation from Mobile Interaction\",\"authors\":\"Aske Mottelson, Jarrod Knibbe, K. Hornbæk\",\"doi\":\"10.1145/3173574.3174135\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":20512,\"journal\":{\"name\":\"Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems\",\"volume\":\"19 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-04-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3173574.3174135\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3173574.3174135","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Veritaps: Truth Estimation from Mobile Interaction
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.
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