# textttBluePrint: A Social Media User Dataset for LLM Persona Evaluation and Training

arXiv: https://arxiv.org/abs/2510.02343 · HTML: https://mlsanity.com/paper/2510.02343/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 31 references that are in the map.
- First posted on arXiv 27 Sep 2025.

## Builds on (most cited first, 19 of 19)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Personalizing Dialogue Agents: I have a dog, do you have pets too?](https://mlsanity.com/paper/1801.07243/) (2018)
- [Whose Opinions Do Language Models Reflect?](https://mlsanity.com/paper/2303.17548/) (2023)
- [AgentSociety: Large-Scale Simulation of LLM-Driven Generative Agents Advances Understanding of Human Behaviors and Society](https://mlsanity.com/paper/2502.08691/) (2025)
- [User Behavior Simulation with Large Language Model based Agents](https://mlsanity.com/paper/2306.02552/) (2023)
- [From Individual to Society: A Survey on Social Simulation Driven by Large Language Model-based Agents](https://mlsanity.com/paper/2412.03563/) (2024)
- [Personalized Dialogue Generation with Diversified Traits](https://mlsanity.com/paper/1901.09672/) (2019)
- [On the steerability of large language models toward data-driven personas](https://mlsanity.com/paper/2311.04978/) (2023)
- [Neeko: Leveraging Dynamic LoRA for Efficient Multi-Character Role-Playing Agent](https://mlsanity.com/paper/2402.13717/) (2024)
- [Adapting Large Language Models to Domains via Reading Comprehension](https://mlsanity.com/paper/2309.09530/) (2023)
- [CommunityLM: Probing Partisan Worldviews from Language Models](https://mlsanity.com/paper/2209.07065/) (2022)
- [Character is Destiny: Can Role-Playing Language Agents Make Persona-Driven Decisions?](https://mlsanity.com/paper/2404.12138/) (2024)
- [Do Large Language Models Solve the Problems of Agent-Based Modeling? A Critical Review of Generative Social Simulations](https://mlsanity.com/paper/2504.03274/) (2025)
- [A Simulation System Towards Solving Societal-Scale Manipulation](https://mlsanity.com/paper/2410.13915/) (2024)
- [Can LLMs Simulate Social Media Engagement? A Study on Action-Guided Response Generation](https://mlsanity.com/paper/2502.12073/) (2025)
- [Characterizing LLM-driven Social Network: The Chirper.ai Case](https://mlsanity.com/paper/2504.10286/) (2025)
- [Mapping the Global Election Landscape on Social Media in 2024](https://mlsanity.com/paper/2406.04962/) (2024)
- [Fine-Grained Behavior Simulation with Role-Playing Large Language Model on Social Media](https://mlsanity.com/paper/2412.03148/) (2024)
- [Regional and Temporal Patterns of Partisan Polarization during the COVID-19 Pandemic in the United States and Canada](https://mlsanity.com/paper/2407.02807/) (2024)

## Built on by (most cited first, 6 of 6)

- [Computational Turing Test Reveals Systematic Differences Between Human and AI Language](https://mlsanity.com/paper/2511.04195/) (2025)
- [Polarization by Default: Auditing Recommendation Bias in LLM-Based Content Curation](https://mlsanity.com/paper/2604.15937/) (2026)
- [Predicting Social Media User Actions: A Hybrid Approach for Common and Rare Behavior Prediction on Bluesky](https://mlsanity.com/paper/2511.17241/) (2025)
- [The textitSilicon Society Cookbook: Design Space of LLM-based Social Simulations](https://mlsanity.com/paper/2605.00197/) (2026)
- [RedditPersona: A Modular Framework for Community-Conditioned LLM Adaptation from Reddit](https://mlsanity.com/paper/2606.06027/) (2026)
- [From Memory to Behavior: A Behavior-Aware Role-Playing Framework for Social Media Influencers](https://mlsanity.com/paper/2609.21349/) (2026)

## Abstract (arXiv)

Large language models (LLMs) offer promising capabilities for simulating social media dynamics at scale, enabling studies that would be ethically or logistically challenging with human subjects. However, the field lacks standardized data resources for fine-tuning and evaluating LLMs as realistic social media agents. We address this gap by introducing SIMPACT, the SIMulation-oriented Persona and Action Capture Toolkit, a privacy respecting framework for constructing behaviorally-grounded social media datasets suitable for training agent models. We formulate next-action prediction as a task for training and evaluating LLM-based agents and introduce metrics at both the cluster and population levels to assess behavioral fidelity and stylistic realism. As a concrete implementation, we release BluePrint, a large-scale dataset built from public Bluesky data focused on political discourse. BluePrint clusters anonymized users into personas of aggregated behaviours, capturing authentic engagement patterns while safeguarding privacy through pseudonymization and removal of personally identifiable information. The dataset includes a sizable action set of 12 social media interaction types (likes, replies, reposts, etc.), each instance tied to the posting activity preceding it. This supports the development of agents that use context-dependence, not only in the language, but also in the interaction behaviours of social media to model social media users. By standardizing data and evaluation protocols, SIMPACT provides a foundation for advancing rigorous, ethically responsible social media simulations. BluePrint serves as both an evaluation benchmark for political discourse modeling and a template for building domain specific datasets to study challenges such as misinformation and polarization.