AI Insight
This study introduces PACIFIER, a graph-learning framework designed to reduce opinion polarization in online social networks by strategically selecting which users to moderate. Using the Friedkin-Johnsen model, the system learns to create intervention sequences that can neutralize either internal beliefs or expressed opinions of selected users. When trained on small synthetic networks (under 50 nodes), PACIFIER successfully transfers to real Twitter networks with up to 155,599 nodes, achieving up to 35.3% improvement over existing methods while running approximately 600 times faster than traditional approaches.
Why it matters
The framework provides a scalable, automated approach to moderating polarized online communities without requiring constant feedback or recalculation after each intervention. This could help social media platforms more efficiently identify key individuals whose moderation would most effectively reduce echo chambers and divisive discourse, with potential applications in community management and platform governance.
Understand the Science
⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
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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 https://github.com/RickyYY-SZU/PACIFIER
Source: PACIFIER: Pacing Opinion Depolarization via a Unified Graph Learning Framework