Two-Faced Humans on Twitter and Facebook: Harvesting Social Multimedia for Human Personality Profiling

TL;DR

This paper introduces a novel multi-view fusion framework called PERS for inferring Myers-Briggs personality types from multi-source social multimedia data, demonstrating the importance of data modality and source selection.

Contribution

It presents one of the first datasets and a new fusion method for multi-modal social media data to improve personality profiling accuracy.

Findings

PERS effectively learns from diverse social multimedia data.

Data source and modality significantly impact machine learning performance.

People reveal different personality facets across social media platforms.

Abstract

Human personality traits are the key drivers behind our decision-making, influencing our life path on a daily basis. Inference of personality traits, such as Myers-Briggs Personality Type, as well as an understanding of dependencies between personality traits and users' behavior on various social media platforms is of crucial importance to modern research and industry applications. 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, the research efforts on personality profiling from multi-source multi-modal social media data are relatively sparse, and the level of impact of different social network data on machine learning performance has yet to be comprehensively evaluated. Furthermore, there is not such dataset in the research community to benchmark. This study is one of the first attempts towards bridging such an important research gap. Specifically, in this work, we infer the Myers-Briggs Personality Type indicators, by applying a novel multi-view fusion framework, called "PERS" and comparing the performance results not just across data modalities but also with respect to different social network data sources. Our experimental results demonstrate the PERS's ability to learn from multi-view data for personality profiling by efficiently leveraging on the significantly different data arriving from diverse social multimedia sources. We have also found that the selection of a machine learning approach is of crucial importance when choosing social network data sources and that people tend to reveal multiple facets of their personality in different social media avenues. Our released social multimedia dataset facilitates future research on this direction.