Automatic personality identification using writing behaviours: an exploratory study

The ability of automatically identifying users' personality is an important part of building adaptive systems and providing personalised services. However, there is still a lack of evaluation methods which can not only unobtrusively gather user data without supplement equipment, but also provide accurate and real-time prediction of users' personality. This paper presents a new approach to identifying personality by combining writing features and machine learning techniques. We conducted an exploratory study where we collected participants' handwriting information and personality information via questionnaire. From these data, we extracted writing features and created classifiers for seven personality dimensions. Our top results include a unique set of writing features which could be predictive of personality and binary classifiers for the seven personality dimensions, with accuracies ranging from 62.5% to 83.9%. These results show that writing features are useful for personality identification when suitable classifiers are adopted.