Large Language Model-driven Multi-Agent Simulation for News Diffusion Under Different Network Structures

• URL: http://arxiv.org/abs/2410.13909v1
• Date: Wed, 16 Oct 2024 23:58:26 GMT
• Title: Large Language Model-driven Multi-Agent Simulation for News Diffusion Under Different Network Structures
• Authors: Xinyi Li, Yu Xu, Yongfeng Zhang, Edward C. Malthouse,
• Abstract summary: This work employs a large language model (LLM)-driven multi-agent simulation to replicate complex interactions within information ecosystems. We investigate key factors that facilitate news propagation, such as agent personalities and network structures. We evaluate three countermeasure strategies, discovering brute-force blocking influential agents in the network or announcing news accuracy can effectively mitigate misinformation.
• Score: 36.45109260662318
• License:
• Abstract: The proliferation of fake news in the digital age has raised critical concerns, particularly regarding its impact on societal trust and democratic processes. Diverging from conventional agent-based simulation approaches, this work introduces an innovative approach by employing a large language model (LLM)-driven multi-agent simulation to replicate complex interactions within information ecosystems. We investigate key factors that facilitate news propagation, such as agent personalities and network structures, while also evaluating strategies to combat misinformation. Through simulations across varying network structures, we demonstrate the potential of LLM-based agents in modeling the dynamics of misinformation spread, validating the influence of agent traits on the diffusion process. Our findings emphasize the advantages of LLM-based simulations over traditional techniques, as they uncover underlying causes of information spread -- such as agents promoting discussions -- beyond the predefined rules typically employed in existing agent-based models. Additionally, we evaluate three countermeasure strategies, discovering that brute-force blocking influential agents in the network or announcing news accuracy can effectively mitigate misinformation. However, their effectiveness is influenced by the network structure, highlighting the importance of considering network structure in the development of future misinformation countermeasures.

Related papers

• Interpreting token compositionality in LLMs: A robustness analysis [10.777646083061395]
  Constituent-Aware Pooling (CAP) is a methodology designed to analyse how large language models process linguistic structures. CAP intervenes in model activations through constituent-based pooling at various model levels.
  arXiv  Detail & Related papers  (2024-10-16T18:10:50Z)
• Online Multi-modal Root Cause Analysis [61.94987309148539]
  Root Cause Analysis (RCA) is essential for pinpointing the root causes of failures in microservice systems. Existing online RCA methods handle only single-modal data overlooking, complex interactions in multi-modal systems. We introduce OCEAN, a novel online multi-modal causal structure learning method for root cause localization.
  arXiv  Detail & Related papers  (2024-10-13T21:47:36Z)
• Neural Networks Decoded: Targeted and Robust Analysis of Neural Network Decisions via Causal Explanations and Reasoning [9.947555560412397]
  We introduce TRACER, a novel method grounded in causal inference theory to estimate the causal dynamics underpinning DNN decisions. Our approach systematically intervenes on input features to observe how specific changes propagate through the network, affecting internal activations and final outputs. TRACER further enhances explainability by generating counterfactuals that reveal possible model biases and offer contrastive explanations for misclassifications.
  arXiv  Detail & Related papers  (2024-10-07T20:44:53Z)
• Decentralized Learning Strategies for Estimation Error Minimization with Graph Neural Networks [94.2860766709971]
  We address the challenge of sampling and remote estimation for autoregressive Markovian processes in a wireless network with statistically-identical agents. Our goal is to minimize time-average estimation error and/or age of information with decentralized scalable sampling and transmission policies.
  arXiv  Detail & Related papers  (2024-04-04T06:24:11Z)
• Transforming Competition into Collaboration: The Revolutionary Role of Multi-Agent Systems and Language Models in Modern Organizations [0.0]
  This article explores the influence of computational entities based on multi-agent systems theory (SMA) and large language models (LLM) on human user interaction. In our approach we employ agents developed from large language models (LLM), each with distinct prototyping that considers behavioral elements. We demonstrate the potential of developing agents useful for organizational strategies, based on multi-agent system theories (SMA) and innovative uses based on large language models (LLM based)
  arXiv  Detail & Related papers  (2024-03-12T15:56:10Z)
• Digital cloning of online social networks for language-sensitive agent-based modeling of misinformation spread [0.0]
  We develop a simulation framework for studying misinformation spread within online social networks. We create a 'digital clone' of a known misinformation sharing network by downloading social media histories for over ten thousand of its users.
  arXiv  Detail & Related papers  (2024-01-23T06:02:03Z)
• Collaborative Information Dissemination with Graph-based Multi-Agent Reinforcement Learning [2.9904113489777826]
  This paper introduces a Multi-Agent Reinforcement Learning (MARL) approach for efficient information dissemination. We propose a Partially Observable Game (POSG) for information dissemination empowering each agent to decide on message forwarding independently. Our experimental results show that our trained policies outperform existing methods.
  arXiv  Detail & Related papers  (2023-08-25T21:30:16Z)
• Preference Enhanced Social Influence Modeling for Network-Aware Cascade Prediction [59.221668173521884]
  We propose a novel framework to promote cascade size prediction by enhancing the user preference modeling. Our end-to-end method makes the user activating process of information diffusion more adaptive and accurate.
  arXiv  Detail & Related papers  (2022-04-18T09:25:06Z)
• Explainable Adversarial Attacks in Deep Neural Networks Using Activation Profiles [69.9674326582747]
  This paper presents a visual framework to investigate neural network models subjected to adversarial examples. We show how observing these elements can quickly pinpoint exploited areas in a model.
  arXiv  Detail & Related papers  (2021-03-18T13:04:21Z)
• Network Diffusions via Neural Mean-Field Dynamics [52.091487866968286]
  We propose a novel learning framework for inference and estimation problems of diffusion on networks. Our framework is derived from the Mori-Zwanzig formalism to obtain an exact evolution of the node infection probabilities. Our approach is versatile and robust to variations of the underlying diffusion network models.
  arXiv  Detail & Related papers  (2020-06-16T18:45:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.