Personality Detection and Analysis using Twitter Data

TL;DR

This paper introduces a large-scale Twitter dataset with 152 million tweets for Myers-Briggs personality prediction, providing extensive analysis and baseline evaluations to advance computational personality detection.

Contribution

The paper presents the largest curated Twitter dataset for personality analysis and offers comprehensive qualitative and quantitative studies along with baseline performance evaluations.

Findings

Data patterns align with natural intuition

Baseline models provide initial performance benchmarks

Extensive dataset enables better understanding of personality signals

Abstract

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.