  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.
