Big-Five Personality Prediction Based on User Behaviors at Social Network Sites

Beyond being facilitators of human interactions, social networks have become an interesting target of research, providing rich information for studying and modeling user’s behavior. Identification of personality-related indicators encrypted in Facebook profiles and activities are of special concern in our current research efforts. This paper explores the feasibility of modeling user personality based on a proposed set of features extracted from the Facebook data. The encouraging results of our study, exploring the suitability and performance of several classification techniques, will also be presented.

Assessment of job performance, personalized health and psychometric measures are domains where data-driven and ubiquitous computing exhibits the potential of a profound impact in the near future. Existing techniques use data extracted from questionnaires, sensors (wearable, computer, etc.), or other traits, to assess well-being and cognitive attributes of individuals. However, these techniques can neither predict individuals wellbeing and psychological traits in a global manner nor consider the challenges associated to processing the data available, that is incomplete and noisy. In this paper, we create a benchmark for predictive analysis of individuals from a perspective that integrates: physical and physiological behavior, psychological states and traits, and job performance. We design data mining techniques as benchmark and uses real noisy and incomplete data derived from wearable sensors to predict 19 constructs based on 12 standardized well-validated tests. The study included 757 participants who were knowledge workers in organizations across the USA with varied work roles. We developed a data mining framework to extract the meaningful predictors for each of the 19 variables under consideration. Our model is the first benchmark that combines these various instrument-derived variables in a single framework to understand people's behavior by leveraging real uncurated data from wearable, mobile, and social media sources. We verify our approach experimentally using the data obtained from our longitudinal study. The results show that our framework is consistently reliable and capable of predicting the variables under study better than the baselines when prediction is restricted to the noisy/incomplete data.

In the Workshop on Computational Personality Recognition (Shared Task), we released two datasets, varying in size and genre, annotated with gold standard personality labels. This allowed participants to evaluate features and learning techniques, and even to compare the performances of their systems for personality recognition on a common benchmark. We had 8 participants to the task. In this paper we discuss the results and compare them to previous literature.