PACIFIER: Pacing Opinion Depolarization via a Unified Graph Learning Framework

Abstract:Online social networks often form opposing echo chambers that reinforce opinion polarization. Under the Friedkin-Johnsen (FJ) model, ModerateInternal (MI) and ModerateExpressed (ME) reduce polarization by neutralizing selected users' internal or expressed opinions, but existing solutions are largely model-specific and learning-based depolarization remains underexplored. We study FJ-based moderation as feedback-free autoregressive sequential planning and propose PACIFIER, a unified graph-learning framework that constructs ordered intervention sequences from the initial graph-opinion instance without using recomputed post-intervention expressed opinions as intermediate feedback. PACIFIER combines history-aware node representations with Greedy and RL variants for immediate and long-horizon action scoring, and supports MI, ME, continuous opinions, cost-aware moderation, and node removal. We evaluate intervention trajectories by Accumulated Normalized Polarization (ANP). Trained only on synthetic graphs with fewer than 50 nodes, PACIFIER transfers to 15 real-world Twitter networks with up to 155,599 nodes. It improves over the strongest non-PACIFIER baseline by up to 35.3%, while PACIFIER-RL improves over PACIFIER-Greedy by up to 37.69%. Compared with oracle-style Greedy, PACIFIER retains 98.94% and 97.39% near-oracle quality on MI and ME, respectively, and is about 600x faster at the 450-node range. The source code is available at this https URL

11 pages. Accepted at the 35th ACM International Conference on Information and Knowledge Management (CIKM 2026)

Social and Information Networks (cs.SI); Machine Learning (cs.LG)

arXiv:2602.23390 [cs.SI]

(or arXiv:2602.23390v4 [cs.SI] for this version)

https://doi.org/10.48550/arXiv.2602.23390 Focus to learn more arXiv-issued DOI via DataCite

https://doi.org/10.1145/3799682.3840684 Focus to learn more DOI(s) linking to related resources

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