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        <title>Predicting Authorship and Author Traits from Keystroke Dynamics</title>
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        <namePart type="given">Barbara</namePart>
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            <title>Proceedings of the Second Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media</title>
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        <name type="personal">
            <namePart type="given">Malvina</namePart>
            <namePart type="family">Nissim</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Viviana</namePart>
            <namePart type="family">Patti</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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            <namePart type="family">Wagner</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T Predicting Authorship and Author Traits from Keystroke Dynamics
%A Plank, Barbara
%Y Nissim, Malvina
%Y Patti, Viviana
%Y Plank, Barbara
%Y Wagner, Claudia
%S Proceedings of the Second Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana, USA
%F plank-2018-predicting
%X 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.
%R 10.18653/v1/W18-1113
%U https://aclanthology.org/W18-1113
%U https://doi.org/10.18653/v1/W18-1113
%P 98-104

Markdown (Informal)

[Predicting Authorship and Author Traits from Keystroke Dynamics](https://aclanthology.org/W18-1113) (Plank, PEOPLES 2018)

• Predicting Authorship and Author Traits from Keystroke Dynamics (Plank, PEOPLES 2018)

ACL

• Barbara Plank. 2018. Predicting Authorship and Author Traits from Keystroke Dynamics. In Proceedings of the Second Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media, pages 98–104, New Orleans, Louisiana, USA. Association for Computational Linguistics.