Agent-Based Exploration of Recommendation Systems in Misinformation Propagation

TL;DR

This paper uses agent-based modeling to analyze how different recommendation algorithms influence misinformation spread on social networks, revealing that algorithm choice significantly impacts exposure to fake content.

Contribution

It introduces an agent-based simulation framework to compare the effects of various recommendation strategies on misinformation propagation, highlighting the importance of algorithm design.

Findings

Popularity-based algorithms amplify misinformation

Content-based filtering reduces fake content exposure

Item-based collaborative filtering outperforms previous methods

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.