The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Detecting Liars in Chats using Keystroke Dynamics
Abstract
In this paper we will investigate the possibilities for detecting liars in chat rooms who have taken on a different identity. While using a different identity people might require more time to reply to questions of the chat partner, or might use corrections to change their text to avoid inconsistencies in their answers. These issues will cause differences in the typing behavior, which can be measured in the typing rhythm. We have shown in this paper that, with a high accuracy, we can distinguish between a chat of a person who uses his/her own identity and is honest in his/her answers, and a chat of a person who is lying because his/her answers need to be consistent to an assumed identity. We obtained a correct classification of a single message in a chat with an accuracy of more than 70% and a correct classification of a full chat with well over 90% accuracy.
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
Borj and Bours used KD feature vectors to detect authors that lied about their demographic information, such as age and gender, in chat conversations.
“Borj and Bours [50] used KD feature vectors to detect authors that lied about their demographic information, such as age and gender, in chat conversations.”
From Online grooming detection: A comprehensive survey of child exploitation in chat logs (2023) · cites this paper as [50]
Predators might conceal the gender or age while making initial contact.
“For example, they might conceal the gender or age while making initial contact [48,50].”
From Online grooming detection: A comprehensive survey of child exploitation in chat logs (2023) · cites this paper as [50]
Predators often use a fake identity while online searching for potential victims.
“It is known that predators often use a fake identity while online searching for potential victims.”
From Online grooming detection: A comprehensive survey of child exploitation in chat logs (2023) · cites this paper as [50]
Despite syntactic complexity correlating with the deceiver's age, some research studies demonstrated that deceivers can create less complex sentences for grooming purposes.
“For instance, despite syntactic complexity correlating with the deceiver's age, some research studies demonstrated that deceivers can create less complex sentences for grooming purposes [50,158,159].”
From Online grooming detection: A comprehensive survey of child exploitation in chat logs (2023) · cites this paper as [50]
Citations
Cites
Identifying emotional states using keystroke dynamics
Epp, C., Lippold, M., & Mandryk, R. L. (2011) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/1978942.1979046 · Cited by 364
A new soft biometric approach for keystroke dynamics based on gender recognition
Giot, R., & Rosenberger, C. (2012) · International Journal of Information Technology and Management · doi:10.1504/ijitm.2012.044062 · Cited by 62
Using keystroke dynamics for gender identification in social network environment
Fairhurst, M., & Da Costa-Abreu, M. (2011) · 4th International Conference on Imaging for Crime Detection and Prevention 2011 (ICDP 2011) · doi:10.1049/ic.2011.0124 · Cited by 37
Flagged first, then by citations
Research works printed but not linked: 19
- 10.3115/v1/D14-1155 · DOI
- 10.1145/2449396.2449426 · DOI
- D.B. Buller and J. K Burgoon . 1996. Interpersonal deception theory. Communication theory, 6(3):203--242 , 1996 . D.B. Buller and J. K Burgoon. 1996. Interpersonal deception theory. Communication theory, 6(3):203--242, 1996.
- 10.1145/2499962.2499967 · DOI
- P. Ekman and W.V. Friesen. 1969. Nonverbal leakage and clues to deception. Psychiatry 3281):88--106 1969. P. Ekman and W.V. Friesen. 1969. Nonverbal leakage and clues to deception. Psychiatry 3281):88--106 1969. · DOI · Paper page
- G.M. Grimes , J.L. Jenkins , and J.S. Valacich . 2013. Assessing credibility by monitoring changes in typing behavior: the keystroke dynamics deception detection model . In Hawaii International Conference on System Sciences, Deception Detection Symposium , 2013 . G.M. Grimes, J.L. Jenkins, and J.S. Valacich. 2013. Assessing credibility by monitoring changes in typing behavior: the keystroke dynamics deception detection model. In Hawaii International Conference on System Sciences, Deception Detection Symposium, 2013.
- 10.1109/HSI.2013.6577879 · DOI · Paper page
- 10.1109/HSI.2015.7170682 · DOI · Paper page
- M. Monaro C. Galante R. Spolaor Q.Q. Li L. Gamberini M. Conti and G. Sartori. 2018. Covert lie detection using keyboard dynamics. Scientific reports 8(1) page 1976 2018. M. Monaro C. Galante R. Spolaor Q.Q. Li L. Gamberini M. Conti and G. Sartori. 2018. Covert lie detection using keyboard dynamics. Scientific reports 8(1) page 1976 2018. · DOI
- S. Mondal , P. Bours , L. Johansen , R. Stenvi , and M. Øverbø . 2017. Importance of a Versatile Logging Tool for Behavioural Biometrics and Continuous Authentication Research , chapter 13, pages 282 -- 305 . IGI Global , 2017 . S. Mondal, P. Bours, L. Johansen, R. Stenvi, and M. Øverbø. 2017. Importance of a Versatile Logging Tool for Behavioural Biometrics and Continuous Authentication Research, chapter 13, pages 282--305. IGI Global, 2017.
- 10.1145/3099023.3099105 · DOI · Paper page
- 10.1016/B978-0-12-812729-2.00010-0 · DOI
- 10.1016/j.diin.2018.01.018 · DOI · Paper page
- 10.4018/IJDCF.2017010101 · DOI · Paper page
- B. van Bockstaele B. Verschuere T. Moens K. Suchotzki E. Debey and A. Spruyt. 2012. Learning to lie: effects of practice on the cognitive cost of lying. Frontiers in psychology 3:256 2012. B. van Bockstaele B. Verschuere T. Moens K. Suchotzki E. Debey and A. Spruyt. 2012. Learning to lie: effects of practice on the cognitive cost of lying. Frontiers in psychology 3:256 2012. · DOI
- 10.1016/j.sbspro.2014.03.335 · DOI
- B. Verschuere , and J. de Houwer . 2011. Detecting concealed information in less than a second: response latency-based measures. Memory detection: Theory and application of the Concealed Information Test , pages 46 -- 62 , 2011 . B. Verschuere, and J. de Houwer. 2011. Detecting concealed information in less than a second: response latency-based measures. Memory detection: Theory and application of the Concealed Information Test, pages 46--62, 2011. · DOI
- 10.1177/0963721410391245 · DOI
- J. J. Walczyk F.D. Igou L.P. Dixon and T. Tcholakian. 2013. Advancing Lie detection by inducing cognitive load on liars: a review of relevant theories and techniques guided by lessons from polygraph-based approaches. Frontiers in psychology 4:14 2013. J. J. Walczyk F.D. Igou L.P. Dixon and T. Tcholakian. 2013. Advancing Lie detection by inducing cognitive load on liars: a review of relevant theories and techniques guided by lessons from polygraph-based approaches. Frontiers in psychology 4:14 2013. · DOI
Cited by
Sorted by citations
Online grooming detection: A comprehensive survey of child exploitation in chat logs
Borj, P. R., Raja, K., & Bours, P. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.110039 · Cited by 38
Self-citation
Metadata
- Authors
- Borj, P. R., & Bours, P.
- Affiliations
- Norwegian University of Science and Technology (NO)
- Venue
- Proceedings of the 2019 3rd International Conference on Biometric Engineering and Applications
- Publisher
- ACM
- Published
- 2019-05-29
- Pages
- 1-6
- OpenAlex
- W2969677212
- Cited by
- 10
- References
- 3
- Citations by year
- 2025: 3 · 2024: 2 · 2023: 1 · 2022: 3 · 2020: 1
- FWCI
- 1.65
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Biometric Engineering and Applications (ICBEA) (publishedVersion)
- Topics
- Social Sciences › Psychology › Social Psychology › Deception detection and forensic psychology
- Physical Sciences › Computer Science › Information Systems › User Authentication and Security Systems
- Physical Sciences › Computer Science › Artificial Intelligence › Authorship Attribution and Profiling
- Keywords
- deception detection, keystroke dynamics, online chat, chat rooms, identity deception, typing behavior, typing rhythm, text correction
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