Personality Traits Prediction Using Social Network Data Analysis

Abstract

Nowadays, using social media platforms like Facebook and Twitter, became a daily activity for millions of people. This extensive usage resulted in generating an enormous amount of users generated content in the form of text, images, and videos known as digital records. Analyzing this content motivated different types of applications among which, is automatic personality recognition. Automatic personality recognition is the process of predicting personality traits using users’ digital data. Recently, many approaches were proposed for the assessment of the positive traits such as the Big Five and negative traits such as the Dark Triad traits. The majority of these approaches focus on the statistical linguistic features while ignoring semantic properties extracted from the user’s text. In addition, current existing features are far away from representing social human activities. In this thesis, an efficient approach for the prediction of the generic positive and negative personality traits using user’s generarted data on social media is proposed. Two contributions are introduced which depend on using knowledge-based semantic features and psychological personality characteristic features. Experimental results show that when using the text semantics, the average accuracy for predicting the Big Five traits reached 60% using Information Content-based measure and 56% when using Path-based measure. For predicting the Big Five and the Dark Triad, the Logistic