Abstract

Technology has a great impact on our everyday lives; computers, smart devices, sensors and digital technology in general, try to communicate with us to accomplish some task. Each step of the communication however, requires understanding of the future behavioral utterance, deciding on what is the circumstance and the social context, and finally predicting the individual’s needs. Even if computers are so deeply involved in our daily lives, they lack basic social skills that would allow for natural communication. We believe automatic personality recognition will provide computers with an essential social notion, improving the quality of services, such as in intelligent tutoring systems or information retrieval systems among many other uses. Over the past few years, researcher in social computing have shown that personality trait recognition from nonverbal behavior is feasible, yet, the accuracy rate never exceeds a certain level, due to a phenomenon called within-person variability. This means that individuals may vary their behavioral manifestation according to the situational context in which they are in. In this thesis, we propose a shift from the traditional personality trait theory, to an approach which incorporates the personality fluctuations. This new perspective defines personality as dynamic episodes, the so called personality states, which relate to situational factors. Based on this property, we define the notion of social situations and propose a fully data-driven approach based on the Topic Modeling theory. The active situational characteristics that emerge from the model are interpreted according to their interrelation to the personality states fluctuations. We also present an automatic framework based on topic modeling, which handles dynamic spatio-temporal patterns of behavior and aims to predict the semantic meaning of the situational patterns, in meaningful situations, without the need of expert annotators.