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Large-Language-Model-Powered Agent-Based Framework for Misinformation and Disinformation Research: Opportunities and Open Challenges

Javier Pastor-Galindo, Pantaleone Nespoli, José A. Ruipérez-Valiente

TL;DR

The paper addresses the threat of mis/disinformation in digital society and proposes a conceptual, LLM-powered agent-based framework with Definition, Simulation, Evaluation, and Exploitation layers to model, simulate, and assess disinformation dynamics and countermeasures. It details five research opportunities for generating generative agent-based social networks, customizable disinformation environments, effect assessment, countermeasure testing, and personalized awareness training, along with three open challenges related to modeling, networks, and disinformation campaigns. By outlining ethical frontiers and practical applications, the work emphasizes safe, interdisciplinary development of sandbox environments for testing defenses, training, and forecasting. The framework aims to enable more effective detection, mitigation, and education against evolving information threats while acknowledging dual-use risks and the need for human-centered design and collaboration.

Abstract

This article presents the affordances that Generative Artificial Intelligence can have in misinformation and disinformation contexts, major threats to our digitalized society. We present a research framework to generate customized agent-based social networks for disinformation simulations that would enable understanding and evaluating the phenomena whilst discussing open challenges.

Large-Language-Model-Powered Agent-Based Framework for Misinformation and Disinformation Research: Opportunities and Open Challenges

TL;DR

The paper addresses the threat of mis/disinformation in digital society and proposes a conceptual, LLM-powered agent-based framework with Definition, Simulation, Evaluation, and Exploitation layers to model, simulate, and assess disinformation dynamics and countermeasures. It details five research opportunities for generating generative agent-based social networks, customizable disinformation environments, effect assessment, countermeasure testing, and personalized awareness training, along with three open challenges related to modeling, networks, and disinformation campaigns. By outlining ethical frontiers and practical applications, the work emphasizes safe, interdisciplinary development of sandbox environments for testing defenses, training, and forecasting. The framework aims to enable more effective detection, mitigation, and education against evolving information threats while acknowledging dual-use risks and the need for human-centered design and collaboration.

Abstract

This article presents the affordances that Generative Artificial Intelligence can have in misinformation and disinformation contexts, major threats to our digitalized society. We present a research framework to generate customized agent-based social networks for disinformation simulations that would enable understanding and evaluating the phenomena whilst discussing open challenges.
Paper Structure (18 sections, 6 figures)

This paper contains 18 sections, 6 figures.

Table of Contents

  1. GENAI: DECODING MIS/DISINFORMATION
  2. LLM-POWERED AGENT-BASED FRAMEWORK FOR MIS/DISINFORMATION RESEARCH
  3. Definition layer
  4. Simulation layer
  5. Evaluation layer
  6. Exploitation layer
  7. RESEARCH OPPORTUNITIES
  8. O1. Generative agent-based social networks
  9. O2. Customizable disinformation environments
  10. O3. Assessment of disinformation effects
  11. O4. Testing of technical countermeasures
  12. O5. Assisting personalized awareness training
  13. OPEN CHALLENGES
  14. C1. Generative agents modeling, simulation and evaluation
  15. C2. Social network modeling, simulation and monitoring
  16. ...and 3 more sections

Figures (6)

  • Figure 1: Layers, elements, opportunities and challenges of the LLM-Powered Agent-based Framework for Disinformation Research
  • Figure 2: Example of a social thread with three agent-based users managed by GPT-4.
  • Figure 3: Example of a context-aware disinformation scenario with an extremist, a troll and a political leader managed by GPT-4.
  • Figure 4: Example of disinformation effects on agents' opinions managed by GPT-4.
  • Figure 5: Example of effects of countermeasures in disinformation environment managed by GPT-4.
  • ...and 1 more figures