H-NeiFi: Non-Invasive and Consensus-Efficient Multi-Agent Opinion Guidance

TL;DR

H-NeiFi is a non-intrusive, multi-agent reinforcement learning framework that guides opinion evolution towards consensus on social media without infringing on user autonomy, improving efficiency and long-term social cohesion.

Contribution

It introduces a hierarchical, non-intrusive opinion guidance model using social roles and adaptive neighbor filtering, optimized with MARL for long-term consensus.

Findings

Increases consensus speed by up to 30.7%.

Maintains global convergence without expert intervention.

Protects user interaction autonomy.

Abstract

The openness of social media enables the free exchange of opinions, but it also presents challenges in guiding opinion evolution towards global consensus. Existing methods often directly modify user views or enforce cross-group connections. These intrusive interventions undermine user autonomy, provoke psychological resistance, and reduce the efficiency of global consensus. Additionally, due to the lack of a long-term perspective, promoting local consensus often exacerbates divisions at the macro level. To address these issues, we propose the hierarchical, non-intrusive opinion guidance framework, H-NeiFi. It first establishes a two-layer dynamic model based on social roles, considering the behavioral characteristics of both experts and non-experts. Additionally, we introduce a non-intrusive neighbor filtering method that adaptively controls user communication channels. Using multi-agent reinforcement learning (MARL), we optimize information propagation paths through a long-term reward function, avoiding direct interference with user interactions. Experiments show that H-NeiFi increases consensus speed by 22.0% to 30.7% and maintains global convergence even in the absence of experts. This approach enables natural and efficient consensus guidance by protecting user interaction autonomy, offering a new paradigm for social network governance.