The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Exploring Keystroke Dynamics and Stylometry Features for Gender Prediction on Chat Data
Abstract
Online anonymity is considered as one of the great gifts of the Internet, but it also brings dangers to society, such as cybercrime, online sexual abuse and bullying, and love scams. Many people are fond of chatting online to make new friends, but how can they be sure that the person sitting behind the other computer is really the person they claim to be? By studying stylometry and keystroke dynamics features from chat data, it is feasible to reveal the actual gender of an online user. In this paper, we examined stylometry and keystroke dynamics features from chat data, and proposed a Random Forest based gender prediction approach by analyzing these features. In order to evaluate the effectiveness of the proposed approach, a data acquisition was conducted to capture the keystroke dynamics and text information when participants were chatting remotely via Skype. All participants were invited to chat freely on any topic they prefered in order to get to know each other. Based on our experimental result, the proposed approach achieved 72% prediction accuracy by analyzing on this free-text data captured only in 15 minutes.
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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 [48]
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 [48]
A combination of text features and keystroke dynamics characteristics can improve authorship profiling performance where texts are short and might not provide enough information for predatory detection.
“It is shown that a combination of both feature sets can improve the authorship profiling performance where the texts are short and might not provide enough information for predatory detection.”
From Online grooming detection: A comprehensive survey of child exploitation in chat logs (2023) · cites this paper as [48]
Li et al. considered the ratio of applying the delete key, the number of letters in the word, and the number of characters in each message to train an RF-based gender prediction model.
“In addition to the timing feature sets, they considered the ratio of applying the delete key, the number of letters in the word, and the number of characters in each message to train an RF-based gender prediction model [48].”
From Online grooming detection: A comprehensive survey of child exploitation in chat logs (2023) · cites this paper as [48]
Li et al. collected chat conversation from Skype and extracted keystroke dynamics and stylometry features to detect the gender of online authors.
“As an example, one can consider the work by Li et al. [48], where they collected the chat conversation from the Skype platform and extracted keystroke dynamics and stylometry features to detect the gender of online authors.”
From Online grooming detection: A comprehensive survey of child exploitation in chat logs (2023) · cites this paper as [48]
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
” How old do you think I am?” A study of language and age in Twitter
Nguyen, D., Gravel, R., Trieschnigg, D., & Meder, T. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v7i1.14381 · Cited by 287
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
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Cited by
Sorted by citations
Tainted Love: a Systematic Literature Review of Online Romance Scam Research
Bilz, A., Shepherd, L. A., & Johnson, G. I. (2023) · Interacting with Computers · doi:10.1093/iwc/iwad048 · Cited by 65
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
- Li, G., Borj, P. R., Bergeron, L., & Bours, P.
- Affiliations
- Norwegian University of Science and Technology (NO)
- Venue
- 2019 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO)
- Publisher
- IEEE
- Published
- 2019-05
- Pages
- 1049-1054
- OpenAlex
- W2957717733
- Cited by
- 9
- References
- 4
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 1 · 2023: 1 · 2022: 2 · 2021: 1 · 2020: 2
- FWCI
- 0.48
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Convention on Information and Communication Technology, Electronics and Microelectronics (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Authorship Attribution and Profiling
- Physical Sciences › Computer Science › Information Systems › User Authentication and Security Systems
- Social Sciences › Linguistics and Language › Digital Communication and Language
- Keywords
- keystroke dynamics, stylometry, gender prediction, online anonymity, user identification, random forest, text features
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