Misinformation Mitigation over Social Networks: a Control Approach

Nicolò Pagan, Andreas Philippou, Giulia De Pasquale
Proceedings of The 8th Annual Learning for Dynamics and Control Conference, PMLR 331:1954-1965, 2026.

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 alongside engagement maximization. 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 goals.

Cite this Paper

BibTeX
@InProceedings{pmlr-v331-pagan26a, title = {Misinformation Mitigation over Social Networks: a Control Approach}, author = {Pagan, Nicol\`o and Philippou, Andreas and Pasquale, Giulia De}, booktitle = {Proceedings of The 8th Annual Learning for Dynamics and Control Conference}, pages = {1954--1965}, year = {2026}, editor = {Sukhatme, Gaurav and Lindemann, Lars and Tu, Stephen and Wierman, Adam and Atanasov, Nikolay}, volume = {331}, series = {Proceedings of Machine Learning Research}, month = {17--19 Jun}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v331/main/assets/pagan26a/pagan26a.pdf}, url = {https://proceedings.mlr.press/v331/pagan26a.html}, 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 alongside engagement maximization. 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 goals.} }
Endnote
%0 Conference Paper %T Misinformation Mitigation over Social Networks: a Control Approach %A Nicolò Pagan %A Andreas Philippou %A Giulia De Pasquale %B Proceedings of The 8th Annual Learning for Dynamics and Control Conference %C Proceedings of Machine Learning Research %D 2026 %E Gaurav Sukhatme %E Lars Lindemann %E Stephen Tu %E Adam Wierman %E Nikolay Atanasov %F pmlr-v331-pagan26a %I PMLR %P 1954--1965 %U https://proceedings.mlr.press/v331/pagan26a.html %V 331 %X 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 alongside engagement maximization. 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 goals.
APA
Pagan, N., Philippou, A. & Pasquale, G.D.. (2026). Misinformation Mitigation over Social Networks: a Control Approach. Proceedings of The 8th Annual Learning for Dynamics and Control Conference, in Proceedings of Machine Learning Research 331:1954-1965 Available from https://proceedings.mlr.press/v331/pagan26a.html.

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