# Fine-Grained Behavior Simulation with Role-Playing Large Language Model on Social Media

arXiv: https://arxiv.org/abs/2412.03148 · HTML: https://mlsanity.com/paper/2412.03148/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Dec 2024.

## Built on by (most cited first, 2 of 2)

- [textttBluePrint: A Social Media User Dataset for LLM Persona Evaluation and Training](https://mlsanity.com/paper/2510.02343/) (2025)
- [PHORECAST: Enabling AI Understanding of Public Health Outreach Across Populations](https://mlsanity.com/paper/2510.02535/) (2025)

## Abstract (arXiv)

Large language models (LLMs) have demonstrated impressive capabilities in role-playing tasks. However, there is limited research on whether LLMs can accurately simulate user behavior in real-world scenarios, such as social media. This requires models to effectively analyze a user's history and simulate their role. In this paper, we introduce FineRob, a novel fine-grained behavior simulation dataset. We collect the complete behavioral history of 1,866 distinct users across three social media platforms. Each behavior is decomposed into three fine-grained elements: object, type, and content, resulting in 78.6k QA records. Based on FineRob, we identify two dominant reasoning patterns in LLMs' behavior simulation processes and propose the OM-CoT fine-tuning method to enhance the capability. Through comprehensive experiments, we conduct an in-depth analysis of key factors of behavior simulation and also demonstrate the effectiveness of OM-CoT approachfootnoteCode and dataset are available at urlhttps://github.com/linkseed18612254945/FineRob