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Authors: Stefano Marrone and Carlo Sansone

Affiliation: Department of Information Technology and Electrical Engineering, University of Naples Federico II, Via Claudio, 21, Napoli, Italy

Keyword(s): Cyberbullying, Keystroke Dynamics, Emotion Recognition, Deep Learning.

Abstract: Recognising users’ emotional states is among the most pursued tasks in the field of affective computing. Despite several works show promising results, they usually require expensive or intrusive hardware. Keystroke Dynamics (KD) is a behavioural biometric, whose typical aim is to identify or confirm the identity of an individual by analysing habitual rhythm patterns as they type on a keyboard. This work focuses on the use of KD as a way to continuously predict users’ emotional states during message writing sessions. In particular, we introduce a time-windowing approach that allows analysing users’ writing sessions in different batches, even when the considered writing window is relatively small. This is very relevant in the field of social media, where the exchanged messages are usually very small and the typing rhythm is very fast. The obtained results suggest that even very short writing windows (in the order of 30”) are sufficient to recognise the subject’s emotional state with th e same level of accuracy of systems based on the analysis of larger writing sessions (i.e., up to a few minutes). (More)

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Paper citation in several formats:
Marrone, S. and Sansone, C. (2022). Identifying Users’ Emotional States through Keystroke Dynamics. In Proceedings of the 3rd International Conference on Deep Learning Theory and Applications - DeLTA; ISBN 978-989-758-584-5; ISSN 2184-9277, SciTePress, pages 207-214. DOI: 10.5220/0011367300003277

@conference{delta22,
author={Stefano Marrone. and Carlo Sansone.},
title={Identifying Users’ Emotional States through Keystroke Dynamics},
booktitle={Proceedings of the 3rd International Conference on Deep Learning Theory and Applications - DeLTA},
year={2022},
pages={207-214},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011367300003277},
isbn={978-989-758-584-5},
issn={2184-9277},
}

TY - CONF

JO - Proceedings of the 3rd International Conference on Deep Learning Theory and Applications - DeLTA
TI - Identifying Users’ Emotional States through Keystroke Dynamics
SN - 978-989-758-584-5
IS - 2184-9277
AU - Marrone, S.
AU - Sansone, C.
PY - 2022
SP - 207
EP - 214
DO - 10.5220/0011367300003277
PB - SciTePress

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