# Y Social: an LLM-powered Social Media Digital Twin

arXiv: https://arxiv.org/abs/2408.00818 · HTML: https://mlsanity.com/paper/2408.00818/

## Key facts

- Cited by 44 papers in the map (data through 14 Oct 2026).
- Builds on 59 of its 161 references that are in the map.
- First posted on arXiv 1 Aug 2024.

## Builds on (most cited first, 30 of 59)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [GoEmotions: A Dataset of Fine-Grained Emotions](https://mlsanity.com/paper/2005.00547/) (2020)
- [Social Simulacra: Creating Populated Prototypes for Social Computing Systems](https://mlsanity.com/paper/2208.04024/) (2022)
- [A Survey on Deep Learning for Named Entity Recognition](https://mlsanity.com/paper/1812.09449/) (2018)
- [S3: Social-network Simulation System with Large Language Model-Empowered Agents](https://mlsanity.com/paper/2307.14984/) (2023)
- [Simulating Opinion Dynamics with Networks of LLM-based Agents](https://mlsanity.com/paper/2311.09618/) (2023)
- [Thinking Fast and Slow in Large Language Models](https://mlsanity.com/paper/2212.05206/) (2022)
- [Evaluating and Inducing Personality in Pre-trained Language Models](https://mlsanity.com/paper/2206.07550/) (2022)
- [An Empirical Survey of Data Augmentation for Limited Data Learning in NLP](https://mlsanity.com/paper/2106.07499/) (2021)
- [The Rise of Social Bots](https://mlsanity.com/paper/1407.5225/) (2014)
- [A Survey on Stance Detection for Mis- and Disinformation Identification](https://mlsanity.com/paper/2103.00242/) (2021)
- [Unveiling the Truth and Facilitating Change: Towards Agent-based Large-scale Social Movement Simulation](https://mlsanity.com/paper/2402.16333/) (2024)
- [Degenerate Feedback Loops in Recommender Systems](https://mlsanity.com/paper/1902.10730/) (2019)
- [Simulating Social Media Using Large Language Models to Evaluate Alternative News Feed Algorithms](https://mlsanity.com/paper/2310.05984/) (2023)
- [Can Large Language Model Agents Simulate Human Trust Behavior?](https://mlsanity.com/paper/2402.04559/) (2024)
- [Stance and Sentiment in Tweets](https://mlsanity.com/paper/1605.01655/) (2016)
- [Do LLM Agents Exhibit Social Behavior?](https://mlsanity.com/paper/2312.15198/) (2023)
- [A Decade of Social Bot Detection](https://mlsanity.com/paper/2007.03604/) (2020)
- [The Persuasive Power of Large Language Models](https://mlsanity.com/paper/2312.15523/) (2023)
- [Understanding Echo Chambers in E-commerce Recommender Systems](https://mlsanity.com/paper/2007.02474/) (2020)
- [Urban Generative Intelligence (UGI): A Foundational Platform for Agents in Embodied City Environment](https://mlsanity.com/paper/2312.11813/) (2023)
- [Network Formation and Dynamics Among Multi-LLMs](https://mlsanity.com/paper/2402.10659/) (2024)
- [Quantifying Controversy in Social Media](https://mlsanity.com/paper/1507.05224/) (2015)
- [Human-AI Coevolution](https://mlsanity.com/paper/2306.13723/) (2023)
- [Bots increase exposure to negative and inflammatory content in online social systems](https://mlsanity.com/paper/1802.07292/) (2018)
- [The Effect of People Recommenders on Echo Chambers and Polarization](https://mlsanity.com/paper/2112.00626/) (2021)
- [Emergence of Scale-Free Networks in Social Interactions among Large Language Models](https://mlsanity.com/paper/2312.06619/) (2023)
- [Open Models, Closed Minds? On Agents Capabilities in Mimicking Human Personalities through Open Large Language Models](https://mlsanity.com/paper/2401.07115/) (2024)

## Built on by (most cited first, 30 of 44)

- [Emergent social conventions and collective bias in LLM populations](https://mlsanity.com/paper/2410.08948/) (2024)
- [From Individual to Society: A Survey on Social Simulation Driven by Large Language Model-based Agents](https://mlsanity.com/paper/2412.03563/) (2024)
- [Human and LLM Biases in Hate Speech Annotations: A Socio-Demographic Analysis of Annotators and Targets](https://mlsanity.com/paper/2410.07991/) (2024)
- [LLM-based Agentic Reasoning Frameworks: A Survey from Methods to Scenarios](https://mlsanity.com/paper/2508.17692/) (2025)
- [Towards a Design Guideline for RPA Evaluation: A Survey of Large Language Model-Based Role-Playing Agents](https://mlsanity.com/paper/2502.13012/) (2025)
- [How Far are LLMs from Being Our Digital Twins? A Benchmark for Persona-Based Behavior Chain Simulation](https://mlsanity.com/paper/2502.14642/) (2025)
- [AgentMisalignment: Measuring the Propensity for Misaligned Behaviour in LLM-Based Agents](https://mlsanity.com/paper/2506.04018/) (2025)
- [Large language models for artificial general intelligence (AGI): A survey of foundational principles and approaches](https://mlsanity.com/paper/2501.03151/) (2025)
- [Unmasking Conversational Bias in AI Multiagent Systems](https://mlsanity.com/paper/2501.14844/) (2025)
- [Real or Robotic? Assessing Whether LLMs Accurately Simulate Qualities of Human Responses in Dialogue](https://mlsanity.com/paper/2409.08330/) (2024)
- [Group size effects and collective misalignment in LLM multi-agent systems](https://mlsanity.com/paper/2510.22422/) (2025)
- [GATSim: Urban Mobility Simulation with Generative Agents](https://mlsanity.com/paper/2506.23306/) (2025)
- [Assessing the Reliability of Persona-Conditioned LLMs as Synthetic Survey Respondents](https://mlsanity.com/paper/2602.18462/) (2026)
- [Can LLMs Simulate Social Media Engagement? A Study on Action-Guided Response Generation](https://mlsanity.com/paper/2502.12073/) (2025)
- [Generative Exaggeration in LLM Social Agents: Consistency, Bias, and Toxicity](https://mlsanity.com/paper/2507.00657/) (2025)
- [Measuring and identifying factors of individuals' trust in Large Language Models](https://mlsanity.com/paper/2502.21028/) (2025)
- [DPRF: A Generalizable Dynamic Persona Refinement Framework for Optimizing Behavior Alignment Between Personalized LLM Role-Playing Agents and Humans](https://mlsanity.com/paper/2510.14205/) (2025)
- [Simulation Agent: A Framework for Integrating Simulation and Large Language Models for Enhanced Decision-Making](https://mlsanity.com/paper/2505.13761/) (2025)
- [Agent-based simulation of online social networks and disinformation](https://mlsanity.com/paper/2512.22082/) (2025)
- [LLMs can persuade only psychologically susceptible humans on societal issues, via trust in AI and emotional appeals, amid logical fallacies](https://mlsanity.com/paper/2604.16935/) (2026)
- [Improving and Assessing the Fidelity of Large Language Models Alignment to Online Communities](https://mlsanity.com/paper/2408.09366/) (2024)
- [Designing Synthetic Discussion Generation Systems: A Case Study for Online Facilitation](https://mlsanity.com/paper/2503.16505/) (2025)
- [Bridging Minds and Machines: Toward an Integration of AI and Cognitive Science](https://mlsanity.com/paper/2508.20674/) (2025)
- [Towards Operational Validation of LLM-Agent Social Simulations: A Replicated Study of a Reddit-like Technology Forum](https://mlsanity.com/paper/2508.21740/) (2025)
- [The Collective Turing Test: Large Language Models Can Generate Realistic Multi-User Discussions](https://mlsanity.com/paper/2511.08592/) (2025)
- [PRISM: A Personality-Driven Multi-Agent Framework for Social Media Simulation](https://mlsanity.com/paper/2512.19933/) (2025)
- [Talk2AI: A Longitudinal Dataset of Human--AI Persuasive Conversations](https://mlsanity.com/paper/2604.04354/) (2026)
- [Mapping how LLMs debate societal issues when shadowing human personality traits, sociodemographics and social media behavior](https://mlsanity.com/paper/2604.27624/) (2026)
- [Inject, Fork, Compare: Defining an Interaction Vocabulary for Multi-Agent Simulation Platforms](https://mlsanity.com/paper/2509.13712/) (2025)
- [Integrating LLM and Diffusion-Based Agents for Social Simulation](https://mlsanity.com/paper/2510.16366/) (2025)

## Abstract (arXiv)

In this paper we introduce Y, a new-generation digital twin designed to replicate an online social media platform. Digital twins are virtual replicas of physical systems that allow for advanced analyses and experimentation. In the case of social media, a digital twin such as Y provides a powerful tool for researchers to simulate and understand complex online interactions. tt Y leverages state-of-the-art Large Language Models (LLMs) to replicate sophisticated agent behaviors, enabling accurate simulations of user interactions, content dissemination, and network dynamics. By integrating these aspects, Y offers valuable insights into user engagement, information spread, and the impact of platform policies. Moreover, the integration of LLMs allows Y to generate nuanced textual content and predict user responses, facilitating the study of emergent phenomena in online environments. To better characterize the proposed digital twin, in this paper we describe the rationale behind its implementation, provide examples of the analyses that can be performed on the data it enables to be generated, and discuss its relevance for multidisciplinary research.