Reading Personality Preferences From Motion Patterns in Computer Mouse Operations

Abstract

Personality not only plays essential roles in people's real lives, but also becomes an important factor for various online services. Traditional approaches to personality assessment usually ask users to answer a long list of questions and are thus not practical in many online systems. It is more desirable to use some universally available data in the system to perform personality assessment. This article reports a controlled study to investigate common mouse operations as a potential new type of data source for online personality assessment. We establish an elaborate personality-mouse behavior dataset from 146 subjects and propose kinematic and adjustment features to characterize mouse motion patterns. Statistical approaches and machine learning algorithms are employed to examine the connections between personality preferences and mouse motion features via correlation analysis, discernibility analysis, and personality recognition experiments. The results reveal some interesting expressions of cognitive personality perspectives reflected in mouse motion patterns, such as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fast starting acceleration</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">better controller</i> . Performance evaluation shows it is possible to recognize different personality preferences using mouse motion features with accuracies ranging from 60.6 to 78.3 percent. Our findings suggest a potential to use mouse operational behaviors as a new data source for personality assessment in various information systems.