Analyzing Facebook Activities for Personality Recognition

Facebook is the largest and the most popular online social network application that records large amount of users’ behavior expressed in various activities such as Facebook likes, status updates, posts, comments, photos, tags and shares. One of the major attractions of such a dataset relates to the predictability of the individuals’ psychological traits from their digital footprints. Such predictions help researchers and service providers to improve personalized offering of products and services. The goal of this work is to investigate the predictability of Facebook users’ personality traits, measured by BIG5 test as a function of their digital records of behavior such as Facebook likes. This research is based on a dataset of 92,255 users who provided their Facebook likes and the results of their BIG5 personality test. As the Facebook likes data includes 600 attributes, the proposed model uses LASSO algorithm to select the best features and to predict Facebook users’ BIG5 personality traits. The best accuracy level of these predictions is achieved for Openness and Extraversion, the lowest accuracy level is obtained for Agreeableness while the accuracy levels of Conscientiousness and Neuroticism are in the middle.