Towards Automatic Personality Prediction Using Facebook Likes Metadata

Abstract

We demonstrate that easy accessible digital records of behavior such as Facebook Likes can be obtained and utilized to automatically distinguish a wide range of highly delicate personal traits such as the Big Five personality traits. The analysis presented based on a dataset of over 738,000 users conferred their Facebook Likes (95 million unique Like objects), social network activities, posts, egocentric network, demographic characteristics, and results of various self-reported psychometric tests. The proposed model uses a new and unique mapping technique between each Facebook Like object to their corresponding Facebook page category/sub-category object extracted from the API calls as Likes metadata, which is then evaluated as features for a set of machine learning algorithms to predict individual psychodemographic profiles from users Likes. Traditionally, entities where able to access an individual’s personality through having them fill out psychological questionnaires. In this paper, we present a method which indicates that a person’s Big Five personality score can be easily predicted by leveraging the information about the pages a person liked on Facebook.