Predicting Authorship and Author Traits from Keystroke Dynamics

Barbara Plank


Abstract
Written text transmits a good deal of nonverbal information related to the author’s identity and social factors, such as age, gender and personality. However, it is less known to what extent behavioral biometric traces transmit such information. We use typist data to study the predictiveness of authorship, and present first experiments on predicting both age and gender from keystroke dynamics. Our results show that the model based on keystroke features, while being two orders of magnitude smaller, leads to significantly higher accuracies for authorship than the text-based system. For user attribute prediction, the best approach is to combine the two, suggesting that extralinguistic factors are disclosed to a larger degree in written text, while author identity is better transmitted in typing behavior.
Anthology ID:
W18-1113
Volume:
Proceedings of the Second Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media
Month:
June
Year:
2018
Address:
New Orleans, Louisiana, USA
Venues:
NAACL | PEOPLES | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
98–104
Language:
URL:
https://www.aclweb.org/anthology/W18-1113
DOI:
10.18653/v1/W18-1113
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PDF:
http://aclanthology.lst.uni-saarland.de/W18-1113.pdf