Keystroke Dynamics Patterns While Writing Positive and Negative Opinions.

Agata Kołakowska,Agnieszka Landowska

doi:10.3390/s21175963

Abstract

This paper deals with analysis of behavioural patterns in human–computer interaction. In the study, keystroke dynamics were analysed while participants were writing positive and negative opinions. A semi-experiment with 50 participants was performed. The participants were asked to recall the most negative and positive learning experiences (subject and teacher) and write an opinion about it. Keystroke dynamics were captured and over 50 diverse features were calculated and checked against the ability to differentiate positive and negative opinions. Moreover, classification of opinions was performed providing accuracy slightly above the random guess level. The second classification approach used self-report labels of pleasure and arousal and showed more accurate results. The study confirmed that it was possible to recognize positive and negative opinions from the keystroke patterns with accuracy above the random guess; however, combination with other modalities might produce more accurate results.

Highlights

This paper deals with analysis of behavioural patterns in human–computer interaction (HCI)
As biometric features are stable over time, behavioural patterns may vary depending on disposition of the day or even moment of the day
An alternative approach is to analyze variability of the patterns from the perspective of finding indicators of human state. We focus on the latter approach and we analyse keystroke dynamics patterns

Summary

Introduction

This paper deals with analysis of behavioural patterns in human–computer interaction (HCI). Among behavioural patterns in HCI an interesting field of study concerns keystroke dynamics and mouse movements as a source of information about a person. Analyzing behavioural patterns from the perspective of human identification, the point of interest is to find stable patterns and eventually deviations from them. An alternative approach is to analyze variability of the patterns from the perspective of finding indicators of human state. The advantage of the keystroke dynamics or mouse movements is that they are natural in HCI and do not require any special hardware. They are not as intrusive as some other methods [1]. It is possible to record the keyboard and mouse parameters during the usual computer usage

Methods

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Conclusion