# From Skepticism to Acceptance: Simulating the Attitude Dynamics Toward Fake News

arXiv: https://arxiv.org/abs/2403.09498 · HTML: https://mlsanity.com/paper/2403.09498/

## Key facts

- Cited by 51 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 38 references that are in the map.
- First posted on arXiv 14 Mar 2024.

## Builds on (most cited first, 18 of 18)

- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [A Diversity-Promoting Objective Function for Neural Conversation Models](https://mlsanity.com/paper/1510.03055/) (2015)
- [Social Simulacra: Creating Populated Prototypes for Social Computing Systems](https://mlsanity.com/paper/2208.04024/) (2022)
- [InCharacter: Evaluating Personality Fidelity in Role-Playing Agents through Psychological Interviews](https://mlsanity.com/paper/2310.17976/) (2023)
- [CharacterEval: A Chinese Benchmark for Role-Playing Conversational Agent Evaluation](https://mlsanity.com/paper/2401.01275/) (2024)
- [Simulating Social Media Using Large Language Models to Evaluate Alternative News Feed Algorithms](https://mlsanity.com/paper/2310.05984/) (2023)
- [CharacterChat: Learning towards Conversational AI with Personalized Social Support](https://mlsanity.com/paper/2308.10278/) (2023)
- [Lyfe Agents: Generative agents for low-cost real-time social interactions](https://mlsanity.com/paper/2310.02172/) (2023)
- [DetermLR: Augmenting LLM-based Logical Reasoning from Indeterminacy to Determinacy](https://mlsanity.com/paper/2310.18659/) (2023)
- [Fortify the Shortest Stave in Attention: Enhancing Context Awareness of Large Language Models for Effective Tool Use](https://mlsanity.com/paper/2312.04455/) (2023)
- [Quantifying the Impact of Large Language Models on Collective Opinion Dynamics](https://mlsanity.com/paper/2308.03313/) (2023)
- [Batch-ICL: Effective, Efficient, and Order-Agnostic In-Context Learning](https://mlsanity.com/paper/2401.06469/) (2024)
- [Masked Thought: Simply Masking Partial Reasoning Steps Can Improve Mathematical Reasoning Learning of Language Models](https://mlsanity.com/paper/2403.02178/) (2024)
- [Improving the Robustness of Summarization Systems with Dual Augmentation](https://mlsanity.com/paper/2306.01090/) (2023)
- [Harnessing Multi-Role Capabilities of Large Language Models for Open-Domain Question Answering](https://mlsanity.com/paper/2403.05217/) (2024)
- [Balancing Information Exposure in Social Networks](https://mlsanity.com/paper/1709.01491/) (2017)
- [A Topic-aware Summarization Framework with Different Modal Side Information](https://mlsanity.com/paper/2305.11503/) (2023)
- [How does Truth Evolve into Fake News? An Empirical Study of Fake News Evolution](https://mlsanity.com/paper/2103.05944/) (2021)

## Built on by (most cited first, 30 of 51)

- [Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems](https://mlsanity.com/paper/2504.01990/) (2025)
- [From Individual to Society: A Survey on Social Simulation Driven by Large Language Model-based Agents](https://mlsanity.com/paper/2412.03563/) (2024)
- [Mobile-Bench: An Evaluation Benchmark for LLM-based Mobile Agents](https://mlsanity.com/paper/2407.00993/) (2024)
- [SocioVerse: A World Model for Social Simulation Powered by LLM Agents and A Pool of 10 Million Real-World Users](https://mlsanity.com/paper/2504.10157/) (2025)
- [Injecting Domain-Specific Knowledge into Large Language Models: A Comprehensive Survey](https://mlsanity.com/paper/2502.10708/) (2025)
- [AgentSense: Benchmarking Social Intelligence of Language Agents through Interactive Scenarios](https://mlsanity.com/paper/2410.19346/) (2024)
- [Decoding Echo Chambers: LLM-Powered Simulations Revealing Polarization in Social Networks](https://mlsanity.com/paper/2409.19338/) (2024)
- [Foundations and Recent Trends in Multimodal Mobile Agents: A Survey](https://mlsanity.com/paper/2411.02006/) (2024)
- [The Truth Becomes Clearer Through Debate! Multi-Agent Systems with Large Language Models Unmask Fake News](https://mlsanity.com/paper/2505.08532/) (2025)
- [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)
- [The Stepwise Deception: Simulating the Evolution from True News to Fake News with LLM Agents](https://mlsanity.com/paper/2410.19064/) (2024)
- [Let Silence Speak: Enhancing Fake News Detection with Generated Comments from Large Language Models](https://mlsanity.com/paper/2405.16631/) (2024)
- [MobileSteward: Integrating Multiple App-Oriented Agents with Self-Evolution to Automate Cross-App Instructions](https://mlsanity.com/paper/2502.16796/) (2025)
- [LMAgent: A Large-scale Multimodal Agents Society for Multi-user Simulation](https://mlsanity.com/paper/2412.09237/) (2024)
- [Is Your LLM-Based Multi-Agent a Reliable Real-World Planner? Exploring Fraud Detection in Travel Planning](https://mlsanity.com/paper/2505.16557/) (2025)
- [Does Socialization Emerge in AI Agent Society? A Case Study of Moltbook](https://mlsanity.com/paper/2602.14299/) (2026)
- [A Cognitive Writing Perspective for Constrained Long-Form Text Generation](https://mlsanity.com/paper/2502.12568/) (2025)
- [The PIMMUR Principles: Ensuring Validity in Collective Behavior of LLM Societies](https://mlsanity.com/paper/2509.18052/) (2025)
- [Weaving Context Across Images: Improving Vision-Language Models through Focus-Centric Visual Chains](https://mlsanity.com/paper/2504.20199/) (2025)
- [MF-LLM: Simulating Population Decision Dynamics via a Mean-Field Large Language Model Framework](https://mlsanity.com/paper/2504.21582/) (2025)
- [AgentGroupChat-V2: Divide-and-Conquer Is What LLM-Based Multi-Agent System Need](https://mlsanity.com/paper/2506.15451/) (2025)
- [LLM-Based Social Simulations Require a Boundary](https://mlsanity.com/paper/2506.19806/) (2025)
- [Social Opinions Prediction Utilizes Fusing Dynamics Equation with LLM-based Agents](https://mlsanity.com/paper/2409.08717/) (2024)
- [SPeCtrum: A Grounded Framework for Multidimensional Identity Representation in LLM-Based Agent](https://mlsanity.com/paper/2502.08599/) (2025)
- [BiDeV: Bilateral Defusing Verification for Complex Claim Fact-Checking](https://mlsanity.com/paper/2502.16181/) (2025)
- [Stop Drawing Scientific Claims from LLM Social Simulations Without Robustness Audits](https://mlsanity.com/paper/2605.18890/) (2026)
- [A Multi-Agent Framework with Automated Decision Rule Optimization for Cross-Domain Misinformation Detection](https://mlsanity.com/paper/2503.23329/) (2025)
- [DEBATE: A Large-Scale Benchmark for Evaluating Opinion Dynamics in Role-Playing LLM Agents](https://mlsanity.com/paper/2510.25110/) (2025)
- [EcoLANG: Efficient and Effective Agent Communication Language Induction for Social Simulation](https://mlsanity.com/paper/2505.06904/) (2025)
- [Beyond Static Testbeds: An Interaction-Centric Agent Simulation Platform for Dynamic Recommender Systems](https://mlsanity.com/paper/2505.16429/) (2025)

## Abstract (arXiv)

In the digital era, the rapid propagation of fake news and rumors via social networks brings notable societal challenges and impacts public opinion regulation. Traditional fake news modeling typically forecasts the general popularity trends of different groups or numerically represents opinions shift. However, these methods often oversimplify real-world complexities and overlook the rich semantic information of news text. The advent of large language models (LLMs) provides the possibility of modeling subtle dynamics of opinion. Consequently, in this work, we introduce a Fake news Propagation Simulation framework (FPS) based on LLM, which studies the trends and control of fake news propagation in detail. Specifically, each agent in the simulation represents an individual with a distinct personality. They are equipped with both short-term and long-term memory, as well as a reflective mechanism to mimic human-like thinking. Every day, they engage in random opinion exchanges, reflect on their thinking, and update their opinions. Our simulation results uncover patterns in fake news propagation related to topic relevance, and individual traits, aligning with real-world observations. Additionally, we evaluate various intervention strategies and demonstrate that early and appropriately frequent interventions strike a balance between governance cost and effectiveness, offering valuable insights for practical applications. Our study underscores the significant utility and potential of LLMs in combating fake news.