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Harnessing Network Effect for Fake News Mitigation: Selecting Debunkers via Self-Imitation Learning

Xiaofei Xu, Ke Deng, Michael Dann, Xiuzhen Zhang

TL;DR

This work tackles multi-stage fake-news mitigation as a reinforcement learning problem with episodic rewards, where the net impact of debunker actions is only observable at campaign end. It introduces NAGASIL, which enhances Generative Adversarial Self-Imitation Learning with a negative sampling regularizer and an augmented state representation to improve sample efficiency and state observability in dynamic social networks. Empirical results on real Facebook and synthetic Twitter networks show that NAGASIL outperforms GASIL and other baselines across budget, stage length, density, and network size, with ablations confirming the additive value of both components. The approach offers a practical, scalable framework for selecting debunkers under budget constraints to curb fake-news spread in complex networked settings.

Abstract

This study aims to minimize the influence of fake news on social networks by deploying debunkers to propagate true news. This is framed as a reinforcement learning problem, where, at each stage, one user is selected to propagate true news. A challenging issue is episodic reward where the "net" effect of selecting individual debunkers cannot be discerned from the interleaving information propagation on social networks, and only the collective effect from mitigation efforts can be observed. Existing Self-Imitation Learning (SIL) methods have shown promise in learning from episodic rewards, but are ill-suited to the real-world application of fake news mitigation because of their poor sample efficiency. To learn a more effective debunker selection policy for fake news mitigation, this study proposes NAGASIL - Negative sampling and state Augmented Generative Adversarial Self-Imitation Learning, which consists of two improvements geared towards fake news mitigation: learning from negative samples, and an augmented state representation to capture the "real" environment state by integrating the current observed state with the previous state-action pairs from the same campaign. Experiments on two social networks show that NAGASIL yields superior performance to standard GASIL and state-of-the-art fake news mitigation models.

Harnessing Network Effect for Fake News Mitigation: Selecting Debunkers via Self-Imitation Learning

TL;DR

This work tackles multi-stage fake-news mitigation as a reinforcement learning problem with episodic rewards, where the net impact of debunker actions is only observable at campaign end. It introduces NAGASIL, which enhances Generative Adversarial Self-Imitation Learning with a negative sampling regularizer and an augmented state representation to improve sample efficiency and state observability in dynamic social networks. Empirical results on real Facebook and synthetic Twitter networks show that NAGASIL outperforms GASIL and other baselines across budget, stage length, density, and network size, with ablations confirming the additive value of both components. The approach offers a practical, scalable framework for selecting debunkers under budget constraints to curb fake-news spread in complex networked settings.

Abstract

This study aims to minimize the influence of fake news on social networks by deploying debunkers to propagate true news. This is framed as a reinforcement learning problem, where, at each stage, one user is selected to propagate true news. A challenging issue is episodic reward where the "net" effect of selecting individual debunkers cannot be discerned from the interleaving information propagation on social networks, and only the collective effect from mitigation efforts can be observed. Existing Self-Imitation Learning (SIL) methods have shown promise in learning from episodic rewards, but are ill-suited to the real-world application of fake news mitigation because of their poor sample efficiency. To learn a more effective debunker selection policy for fake news mitigation, this study proposes NAGASIL - Negative sampling and state Augmented Generative Adversarial Self-Imitation Learning, which consists of two improvements geared towards fake news mitigation: learning from negative samples, and an augmented state representation to capture the "real" environment state by integrating the current observed state with the previous state-action pairs from the same campaign. Experiments on two social networks show that NAGASIL yields superior performance to standard GASIL and state-of-the-art fake news mitigation models.
Paper Structure (17 sections, 2 theorems, 12 equations, 4 figures, 1 algorithm)

This paper contains 17 sections, 2 theorems, 12 equations, 4 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Preliminaries and Problem Statement
  4. Methodology
  5. Self Imitation Learning
  6. NAGASIL
  7. Experiments
  8. Performance w.r.t. Mitigation Settings
  9. Performance w.r.t. Network Settings
  10. Ablation Study
  11. Performance on Larger Twitter Networks
  12. Limitations
  13. Conclusion
  14. Proof of Propositions
  15. Proposition 1.
  16. ...and 2 more sections

Key Result

Proposition 1

Provided that low Q-value state-action pairs appear more often in past bad experiences, $\mathbb{E}[\pi_{\theta}(a|s, s')Q(s, s', a)]\geq \mathbb{E}[\pi_{\theta_1}(a|s, s')Q(s, s', a)]$ where $\pi_{\theta}$ is learnt with the negative samples while $\pi_{\theta_1}$ is learnt without the negative sam

Figures (4)

  • Figure 1: Generative Adversarial Self-Imitation Learning.
  • Figure 2: Performance for rumour mitigation on a real-world Facebook social network.
  • Figure 3: Performance for rumour mitigation on synthetic Twitter networks with various settings.
  • Figure 4: NAGASIL - ablation study and performance on larger networks.

Theorems & Definitions (2)

  • Proposition 1
  • Proposition 2