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Rumor Mitigation in Social Media Platforms with Deep Reinforcement Learning

Hongyuan Su, Yu Zheng, Jingtao Ding, Depeng Jin, Yong Li

TL;DR

The paper tackles rumor mitigation on social networks by blocking a small fraction of edges rather than removing users or broadcasting corrections. It introduces a knowledge-informed DRL framework that combines a link-route-aware GNN with a rumor-oriented policy network, trained in a randomized RL environment to generalize across diverse topologies and rumor sources. Key contributions include designing topology and propagation features, a dual-graph message-passing scheme, and a generalized training protocol that yields substantial reductions in rumor spread on real networks (e.g., over 25% relative improvement over baselines). This approach offers a practical, low-disturbance strategy for rumor containment with scalable performance across large platforms, and it provides insights into which network features most influence mitigation effectiveness.

Abstract

Social media platforms have become one of the main channels where people disseminate and acquire information, of which the reliability is severely threatened by rumors widespread in the network. Existing approaches such as suspending users or broadcasting real information to combat rumors are either with high cost or disturbing users. In this paper, we introduce a novel rumor mitigation paradigm, where only a minimal set of links in the social network are intervened to decelerate the propagation of rumors, countering misinformation with low business cost and user awareness. A knowledge-informed agent embodying rumor propagation mechanisms is developed, which intervenes the social network with a graph neural network for capturing information flow in the social media platforms and a policy network for selecting links. Experiments on real social media platforms demonstrate that the proposed approach can effectively alleviate the influence of rumors, substantially reducing the affected populations by over 25%. Codes for this paper are released at https://github.com/tsinghua-fib-lab/DRL-Rumor-Mitigation.

Rumor Mitigation in Social Media Platforms with Deep Reinforcement Learning

TL;DR

The paper tackles rumor mitigation on social networks by blocking a small fraction of edges rather than removing users or broadcasting corrections. It introduces a knowledge-informed DRL framework that combines a link-route-aware GNN with a rumor-oriented policy network, trained in a randomized RL environment to generalize across diverse topologies and rumor sources. Key contributions include designing topology and propagation features, a dual-graph message-passing scheme, and a generalized training protocol that yields substantial reductions in rumor spread on real networks (e.g., over 25% relative improvement over baselines). This approach offers a practical, low-disturbance strategy for rumor containment with scalable performance across large platforms, and it provides insights into which network features most influence mitigation effectiveness.

Abstract

Social media platforms have become one of the main channels where people disseminate and acquire information, of which the reliability is severely threatened by rumors widespread in the network. Existing approaches such as suspending users or broadcasting real information to combat rumors are either with high cost or disturbing users. In this paper, we introduce a novel rumor mitigation paradigm, where only a minimal set of links in the social network are intervened to decelerate the propagation of rumors, countering misinformation with low business cost and user awareness. A knowledge-informed agent embodying rumor propagation mechanisms is developed, which intervenes the social network with a graph neural network for capturing information flow in the social media platforms and a policy network for selecting links. Experiments on real social media platforms demonstrate that the proposed approach can effectively alleviate the influence of rumors, substantially reducing the affected populations by over 25%. Codes for this paper are released at https://github.com/tsinghua-fib-lab/DRL-Rumor-Mitigation.
Paper Structure (10 sections, 4 equations, 3 figures, 2 tables)

This paper contains 10 sections, 4 equations, 3 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Problem Statement
  3. Method
  4. Knowledge-informed Edge Selection
  5. Generalized Model Training
  6. Experiments
  7. Experiment Settings
  8. Overall Performance
  9. Ablation Study
  10. Conclusion

Figures (3)

  • Figure 1: (Top) The environment provides observations of social network, controls the network based on the actions chosen by the agent, and returns the rewards through the rumor propagation simulations. (Bottom) Knowledge-driven edge selection which aggregates neighbors' information through link-route-aware massage passing on both the original and the line graph using a GNN, and finally selects edges with the rumor-oriented policy network.
  • Figure 2: (a) The cumulative percentage of affected users at each propagation step. (b) The proportion of users who are spreading rumors at each propagation step.
  • Figure 3: The result of ablation experiments, which removes features for nodes (FN) and edges (FE), GNN (Link and Route) and the rumor-oriented design in policy network (Community and Source), respectively. Best viewed in color.