Learning to Control Misinformation: a Closed-loop Approach for Misinformation Mitigation over Social Networks

TL;DR

This paper introduces a control framework for social networks that reduces misinformation spread by penalizing extreme negative sentiment and novelty, while maintaining user engagement, validated through simulations showing significant misinformation reduction.

Contribution

It extends the Friedkin-Johnsen model to jointly optimize misinformation mitigation and user engagement using both model-free and model-based control strategies.

Findings

Up to 76% reduction in misinformation propagation.

Engagement can improve even as misinformation decreases in networks with radical users.

The framework offers practical guidance for balancing misinformation suppression and engagement.

Abstract

Modern social networks rely on recommender systems that inadvertently amplify misinformation by prioritizing engagement over content veracity. We present a control framework that mitigates misinformation spread while maintaining user engagement by penalizing content characteristics commonly exploited by false information, specifically, extreme negative sentiment and novelty. We extend the closed-loop Friedkin-Johnsen model to incorporate the mitigation of misinformation together with the maximization of user engagement. Both model-free and model-based control strategies demonstrate up to 76% reduction in misinformation propagation across diverse network configurations, validated through simulations using the LIAR2 dataset with sentiment features extracted via large language models. Analysis of engagement-misinformation trade-offs reveals that in networks with radical users, median engagement improves even as misinformation decreases, suggesting content moderation enhances discourse quality for non-extremist users. The framework provides practical guidance for platform operators in balancing misinformation suppression with engagement objectives.