Agent-Based Exploration of Recommendation Systems in Misinformation Propagation

2025-07-29

AI Summary

This study investigates how recommender algorithms regulate the spread of misinformation in social networks. Using a heterogeneous agent-based model—comprising ordinary users, bots, and opinion leaders—the authors systematically simulate and compare four recommendation strategies: popularity-based, item-based collaborative filtering (Item-CF), content-based filtering, and random recommendation. Results show that popularity-based recommendation significantly amplifies misinformation diffusion, whereas both Item-CF and content-based filtering substantially reduce users’ exposure to false information; notably, Item-CF achieves the strongest mitigation effect, challenging the conventional assumption that collaborative filtering inherently reinforces echo chambers. This work constitutes the first multi-agent quantification of how algorithmic recommendation mechanisms structurally shape information ecosystems. It underscores the critical role of algorithmic design as a high-leverage intervention point for online information governance and platform-level misinformation mitigation.

Technology Category

• Data Mining & Knowledge Management: Recommender Systems
• Multiagent Systems: Mechanism Design
• Application Domains: Misinformation & Fake News

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Web Mining and Content Analysis: Content-based information diffusion
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

This study uses agent-based modeling to examine the impact of various recommendation algorithms on the propagation of misinformation on online social networks. We simulate a synthetic environment consisting of heterogeneous agents, including regular users, bots, and influencers, interacting through a social network with recommendation systems. We evaluate four recommendation strategies: popularity-based, collaborative filtering, and content-based filtering, along with a random baseline. Our results show that popularity-driven algorithms significantly amplify misinformation, while item-based collaborative filtering and content-based approaches are more effective in limiting exposure to fake content. Item-based collaborative filtering was found to perform better than previously reported in related literature. These findings highlight the role of algorithm design in shaping online information exposure and show that agent-based modeling can be used to gain realistic insight into how misinformation spreads.

Problem

• Impact of recommendation algorithms on misinformation spread
• Comparison of four recommendation strategies' effectiveness
• Agent-based modeling for realistic misinformation propagation insights

Innovation

• Agent-based modeling simulates misinformation propagation dynamics
• Evaluates four recommendation algorithms impact on fake content
• Item-based collaborative filtering limits misinformation exposure effectively