Personality Predictions Based on User Behavior on the Facebook Social Media Platform

• Published in: IEEE access Vol. 6; pp. 61959 - 61969
• Main Authors: Tadesse, Michael M., Lin, Hongfei, Xu, Bo, Yang, Liang
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Algorithms; Big 5; Correlation; Datasets; Digital media; Facebook; feature analysis; Feature extraction; Linguistics; Machine learning; Network analysis; Personality; Personality traits; predicting personality; Predictive models; Psychology; social behavior; Social networks; User behavior
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2018.2876502

With the development of social networks, a large variety of approaches have been developed to define users' personalities based on their social activities and language use habits. Particular approaches differ with regard to different machine learning algorithms, data sources, and feature sets. The goal of this paper is to investigate the predictability of the personality traits of Facebook users based on different features and measures of the Big 5 model. We examine the presence of structures of social networks and linguistic features relative to personality interactions using the myPersonality project data set. We analyze and compare four machine learning models and perform the correlation between each of the feature sets and personality traits. The results for the prediction accuracy show that even if tested under the same data set, the personality prediction system built on the XGBoost classifier outperforms the average baseline for all the feature sets, with a highest prediction accuracy of 74.2%. The best prediction performance was reached for the extraversion trait by using the individual social network analysis features set, which achieved a higher personality prediction accuracy of 78.6%.