Exploring Personality and Online Social Engagement: An Investigation of MBTI Users on Twitter

TL;DR

This study uses deep learning on Twitter data to predict MBTI personality traits, demonstrating that social media text can effectively reveal individual personality profiles, with implications for psychology and computational linguistics.

Contribution

It introduces a novel approach using BERT to predict MBTI traits from social media text, validating the lexical hypothesis in digital communication.

Findings

Biographies, statuses, and liked tweets predict MBTI traits.

Deep learning models outperform traditional methods.

Results support the link between language use and personality.

Abstract

Text-based personality prediction by computational models is an emerging field with the potential to significantly improve on key weaknesses of survey-based personality assessment. We investigate 3848 profiles from Twitter with self-labeled Myers-Briggs personality traits (MBTI) - a framework closely related to the Five Factor Model of personality - to better understand how text-based digital traces from social engagement online can be used to predict user personality traits. We leverage BERT, a state-of-the-art NLP architecture based on deep learning, to analyze various sources of text that hold most predictive power for our task. We find that biographies, statuses, and liked tweets contain significant predictive power for all dimensions of the MBTI system. We discuss our findings and their implications for the validity of the MBTI and the lexical hypothesis, a foundational theory underlying the Five Factor Model that links language use and behavior. Our results hold optimistic implications for personality psychologists, computational linguists, and other social scientists aiming to predict personality from observational text data and explore the links between language and core behavioral traits.