Applying machine learning algorithms for deriving personality traits in social network

Social and cognitive sciences' knowledge about social behavior and social networks combined with the new computational machine learning techniques can facilitate the creation of better models. We propose and evaluate a new methodology for finding personality traits of young adults involved in a network using hyper optimization algorithms. We used a social contagion model for the spread of behavior (measured by the physical activity level) among the participants. A part of the Big-5 questionnaire was used to gather information about people regarding their traits of openness and expressiveness. Then we try to fine tune the model using machine learning algorithms. The fine tuning of questions from an intake questionnaire can be very useful in validating a model. The accuracy delivered by machine learning pure algorithms is shown to be better, but the inclusion of data related to people's traits is beneficial in defining their characteristics.