# Latent Human Traits in the Language of Social Media: An Open-Vocabulary Approach

arXiv: https://arxiv.org/abs/1705.08038 · HTML: https://mlsanity.com/paper/1705.08038/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 May 2017.

## Built on by (most cited first, 7 of 7)

- [Systematic Evaluation of GPT-3 for Zero-Shot Personality Estimation](https://mlsanity.com/paper/2306.01183/) (2023)
- [Open Models, Closed Minds? On Agents Capabilities in Mimicking Human Personalities through Open Large Language Models](https://mlsanity.com/paper/2401.07115/) (2024)
- [Large Human Language Models: A Need and the Challenges](https://mlsanity.com/paper/2312.07751/) (2023)
- [Exploring Personality and Online Social Engagement: An Investigation of MBTI Users on Twitter](https://mlsanity.com/paper/2109.06402/) (2021)
- [You Are What You Talk About: Inducing Evaluative Topics for Personality Analysis](https://mlsanity.com/paper/2302.00493/) (2023)
- [Context-Aware Prediction of User Engagement on Online Social Platforms](https://mlsanity.com/paper/2310.14533/) (2023)
- [Detecting value-expressive text posts in Russian social media](https://mlsanity.com/paper/2312.08968/) (2023)

## Abstract (arXiv)

Over the past century, personality theory and research has successfully identified core sets of characteristics that consistently describe and explain fundamental differences in the way people think, feel and behave. Such characteristics were derived through theory, dictionary analyses, and survey research using explicit self-reports. The availability of social media data spanning millions of users now makes it possible to automatically derive characteristics from language use -- at large scale. Taking advantage of linguistic information available through Facebook, we study the process of inferring a new set of potential human traits based on unprompted language use. We subject these new traits to a comprehensive set of evaluations and compare them with a popular five factor model of personality. We find that our language-based trait construct is often more generalizable in that it often predicts non-questionnaire-based outcomes better than questionnaire-based traits (e.g. entities someone likes, income and intelligence quotient), while the factors remain nearly as stable as traditional factors. Our approach suggests a value in new constructs of personality derived from everyday human language use.