AWS-EP: A Multi-Task Prediction Approach for MBTI/Big5 Personality Tests

Abstract

Personality and preferences are essential variables in computational sociology and social science. They describe differences between people at both individual and group levels. In recent years, automated approaches that detect personality traits have received much attention due to the massive availability of individuals' digital footprints. Furthermore, researchers have demonstrated a strong link between personality traits and various downstream tasks such as personalized filtering, profile categorization, and profile embedding. Therefore, the detection of individuals' preferences has become a critical process for improving the performance of different tasks. In this paper, we build on the importance of the individual's behaviour and propose a novel multitask modeling approach that understands and models the users' personalities based on their textual posts and comments within a multimedia framework. The novelties of our work compared to state-of-the-art personality prediction models are: improving the performance of the Big five-factor model (Big5) personality test using shared information from the Myers Briggs Type Indicator (MBTI) test, and proposing a one personality detection framework that accurately predicts both MBTI and Big5 tests simultaneously. Predicting both tests simultaneously improves the personality detection framework's flexibility to be used for different goals instead of being used only for a unique purpose (whether for the MBTI test or for the Big5 test separately). Experiments and results demonstrate that our solution outperforms state-of-the-art models across multiple famous personality datasets.