# Personality Detection and Analysis using Twitter Data

arXiv: https://arxiv.org/abs/2309.05497 · HTML: https://mlsanity.com/paper/2309.05497/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 46 references that are in the map.
- First posted on arXiv 11 Sep 2023.

## Builds on (most cited first, 10 of 10)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [FastText.zip: Compressing text classification models](https://mlsanity.com/paper/1612.03651/) (2016)
- [Empath: Understanding Topic Signals in Large-Scale Text](https://mlsanity.com/paper/1602.06979/) (2016)
- [Recent Trends in Deep Learning Based Personality Detection](https://mlsanity.com/paper/1908.03628/) (2019)
- [Aspect-based Sentiment Analysis of Scientific Reviews](https://mlsanity.com/paper/2006.03257/) (2020)
- [TweetBERT: A Pretrained Language Representation Model for Twitter Text Analysis](https://mlsanity.com/paper/2010.11091/) (2020)
- [CRUSH: Contextually Regularized and User anchored Self-supervised Hate speech Detection](https://mlsanity.com/paper/2204.06389/) (2022)
- [(Im)balance in the Representation of News? An Extensive Study on a Decade Long Dataset from India](https://mlsanity.com/paper/2110.14183/) (2021)
- [Fast Few shot Self-attentive Semi-supervised Political Inclination Prediction](https://mlsanity.com/paper/2209.10292/) (2022)

## Built on by (most cited first, 1 of 1)

- [Can LLMs Infer Personality from Real World Conversations?](https://mlsanity.com/paper/2507.14355/) (2025)

## Abstract (arXiv)

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.