# Large Language Models Can Infer Psychological Dispositions of Social Media Users

arXiv: https://arxiv.org/abs/2309.08631 · HTML: https://mlsanity.com/paper/2309.08631/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 53 references that are in the map.
- First posted on arXiv 13 Sep 2023.

## Builds on (most cited first, 7 of 7)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings](https://mlsanity.com/paper/1607.06520/) (2016)
- [Whose Opinions Do Language Models Reflect?](https://mlsanity.com/paper/2303.17548/) (2023)
- [Towards Measuring the Representation of Subjective Global Opinions in Language Models](https://mlsanity.com/paper/2306.16388/) (2023)
- [Regulating ChatGPT and other Large Generative AI Models](https://mlsanity.com/paper/2302.02337/) (2023)
- [BiasAsker: Measuring the Bias in Conversational AI System](https://mlsanity.com/paper/2305.12434/) (2023)
- [Estimating the Personality of White-Box Language Models](https://mlsanity.com/paper/2204.12000/) (2022)

## Built on by (most cited first, 29 of 29)

- [The Personality Illusion: Revealing Dissociation Between Self-Reports & Behavior in LLMs](https://mlsanity.com/paper/2509.03730/) (2025)
- [User Modeling in the Era of Large Language Models: Current Research and Future Directions](https://mlsanity.com/paper/2312.11518/) (2023)
- [Exploring the Frontiers of LLMs in Psychological Applications: A Comprehensive Review](https://mlsanity.com/paper/2401.01519/) (2024)
- [A Survey on Human-Centric LLMs](https://mlsanity.com/paper/2411.14491/) (2024)
- [Large Language Models Can Infer Personality from Free-Form User Interactions](https://mlsanity.com/paper/2405.13052/) (2024)
- [Rediscovering the Latent Dimensions of Personality with Large Language Models as Trait Descriptors](https://mlsanity.com/paper/2409.09905/) (2024)
- [Labeling Free-text Data using Language Model Ensembles](https://mlsanity.com/paper/2501.08413/) (2025)
- [The Basic B*** Effect: The Use of LLM-based Agents Reduces the Distinctiveness and Diversity of People's Choices](https://mlsanity.com/paper/2509.02910/) (2025)
- [Algorithmic Persuasion Through Simulation](https://mlsanity.com/paper/2311.18138/) (2023)
- [Static network structure cannot stabilize cooperation among Large Language Model agents](https://mlsanity.com/paper/2411.10294/) (2024)
- [AI-exhibited Personality Traits Can Shape Human Self-concept through Conversations](https://mlsanity.com/paper/2601.12727/) (2026)
- [Context-Aware Prediction of User Engagement on Online Social Platforms](https://mlsanity.com/paper/2310.14533/) (2023)
- [The use of GPT-4o and Other Large Language Models for the Improvement and Design of Self-Assessment Scales for Measurement of Interpersonal Communication Skills](https://mlsanity.com/paper/2409.14050/) (2024)
- [On the Interplay between Musical Preferences and Personality through the Lens of Language](https://mlsanity.com/paper/2508.18208/) (2025)
- [Mapping Patient-Perceived Physician Traits from Nationwide Online Reviews with LLMs](https://mlsanity.com/paper/2510.03997/) (2025)
- [When AI Takes the Couch: Psychometric Jailbreaks Reveal Internal Conflict in Frontier Models](https://mlsanity.com/paper/2512.04124/) (2025)
- [LangLasso: Interactive Cluster Descriptions through LLM Explanation](https://mlsanity.com/paper/2601.10458/) (2026)
- [Personalized Group Relative Policy Optimization for Heterogenous Preference Alignment](https://mlsanity.com/paper/2603.10009/) (2026)
- [LLM Agents Predict Social Media Reactions but Do Not Outperform Text Classifiers: Benchmarking Simulation Accuracy Using 120K+ Personas of 1511 Humans](https://mlsanity.com/paper/2604.19787/) (2026)
- [Who are you, ChatGPT? Personality and Demographic Style in LLM-Generated Content](https://mlsanity.com/paper/2510.11434/) (2025)
- [Can LLMs Assess Personality? Validating Conversational AI for Trait Profiling](https://mlsanity.com/paper/2602.15848/) (2026)
- [Projective Psychological Assessment of Large Multimodal Models Using Thematic Apperception Tests](https://mlsanity.com/paper/2602.17108/) (2026)
- [Psychological Concept Neurons: Can Neural Control Bias Probing and Shift Generation in LLMs?](https://mlsanity.com/paper/2604.11802/) (2026)
- [Can LLMs Infer Conversational Agent Users' Personality Traits from Chat History?](https://mlsanity.com/paper/2604.19785/) (2026)
- [Mechanistic Personality Analysis of LLMs: Steering Personality via Latent Feature Interventions](https://mlsanity.com/paper/2606.28770/) (2026)
- [LLM-powered reasoning in agent-based modeling](https://mlsanity.com/paper/2607.06757/) (2026)
- [Evaluating Personal Information Output from Conversational Interactions in Generative AI Systems](https://mlsanity.com/paper/2609.22204/) (2026)
- [Thinking Less to Simulate Better: Intuitive Prompting Improves LLM Agents Simulating Individual Social Media Reactions, Including Unfamiliar Content](https://mlsanity.com/paper/2609.30563/) (2026)
- [Cross-Protocol Personality Assessment with LLMs: Aligning Behavioral Evidence with Human Ratings](https://mlsanity.com/paper/2610.16050/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) demonstrate increasingly human-like abilities across a wide variety of tasks. In this paper, we investigate whether LLMs like ChatGPT can accurately infer the psychological dispositions of social media users and whether their ability to do so varies across socio-demographic groups. Specifically, we test whether GPT-3.5 and GPT-4 can derive the Big Five personality traits from users' Facebook status updates in a zero-shot learning scenario. Our results show an average correlation of r = .29 (range = [.22, .33]) between LLM-inferred and self-reported trait scores - a level of accuracy that is similar to that of supervised machine learning models specifically trained to infer personality. Our findings also highlight heterogeneity in the accuracy of personality inferences across different age groups and gender categories: predictions were found to be more accurate for women and younger individuals on several traits, suggesting a potential bias stemming from the underlying training data or differences in online self-expression. The ability of LLMs to infer psychological dispositions from user-generated text has the potential to democratize access to cheap and scalable psychometric assessments for both researchers and practitioners. On the one hand, this democratization might facilitate large-scale research of high ecological validity and spark innovation in personalized services. On the other hand, it also raises ethical concerns regarding user privacy and self-determination, highlighting the need for stringent ethical frameworks and regulation.