Personality classification based on profiles of social networks' users and the five-factor model of personality

Online social networks have become demanded ways for users to show themselves and connect and share information with each other among these social networks. Facebook is the most popular social network. Personality recognition is one of the new challenges between investigators in social networks. This paper presents a hypothesis that users by similar personality are expected to display mutual behavioral patterns when cooperating through social networks. With the goal of personality recognition in terms of analyzing user activity within Facebook, we collected information about the personality traits of users and their profiles on Facebook, hence we flourished an application using API Facebook. The participants of this study are 100 volunteers of Facebook users. We asked the participants to respond the NEO personality questionnaire in a period of 1 month in May 2012. At the end of this questionnaire, there was a link that asked the participants to permit the application to access their profiles. Based on all the collected data, classifiers were learned using different data mining techniques to recognize user personality by their profile and without filling out any questionnaire. With comparing classifiers' results, the boosting-decision tree was our proposed model with 82.2% accuracy was more accurate than previous studies that were able to foresee personality according to the variables in their profiles in five factors for using it as a model for recognizing personality.