Do we behave differently on Twitter and Facebook: Multi-view social network user personality profiling for content recommendation.

Abstract

Human personality traits are key drivers behind our decision making, influencing our lives on a daily basis. Inference of personality traits, such as the Myers-Briggs personality type, as well as an understanding of dependencies between personality traits and user behavior on various social media platforms, is of crucial importance to modern research and industry applications such as recommender systems. The emergence of diverse and cross-purpose social media avenues makes it possible to perform user personality profiling automatically and efficiently based on data represented across multiple data modalities. However, research efforts on personality profiling from multi-source multi-modal social media data are relatively sparse; the impact of different social network data on profiling performance and of personality traits on applications such as recommender systems is yet to be evaluated. Furthermore, large-scale datasets are also lacking in the research community. To fill these gaps, in this work we develop a novel multi-view fusion framework PERS that infers Myers-Briggs personality type indicators. We evaluate the results not just across data modalities but also across different social networks, and also evaluate the impact of inferred personality traits on recommender systems. Our experimental results demonstrate that PERS is able to learn from multi-view data for personality profiling by efficiently leveraging highly varied data from diverse social multimedia sources. Furthermore, we demonstrate that inferred personality traits can be beneficial to other industry applications. Among other results, we show that people tend to reveal multiple facets of their personality in different social media avenues. We also release a social multimedia dataset in order to facilitate further research on this direction.