  Personality types are important in various fields as they hold relevant
information about the characteristics of a human being in an explainable
format. They are often good predictors of a person's behaviors in a particular
environment and have applications ranging from candidate selection to marketing
and mental health. Recently automatic detection of personality traits from
texts has gained significant attention in computational linguistics. Most
personality detection and analysis methods have focused on small datasets
making their experimental observations often limited. To bridge this gap, we
focus on collecting and releasing the largest automatically curated dataset for
the research community which has 152 million tweets and 56 thousand data points
for the Myers-Briggs personality type (MBTI) prediction task. We perform a
series of extensive qualitative and quantitative studies on our dataset to
analyze the data patterns in a better way and infer conclusions. We show how
our intriguing analysis results often follow natural intuition. We also perform
a series of ablation studies to show how the baselines perform for our dataset.
