Rumor Detection on Social Media with Reinforcement Learning-based Key Propagation Graph Generator

TL;DR

This paper introduces KPG, a reinforcement learning framework that generates key propagation graphs to improve rumor detection on social media, especially when propagation data is sparse or noisy.

Contribution

The paper proposes a novel RL-based method, KPG, that enhances rumor detection by generating informative propagation patterns, addressing limitations of existing graph-based approaches.

Findings

KPG outperforms state-of-the-art methods on four datasets.

It effectively handles rumors with limited propagation information.

The framework improves detection accuracy in noisy propagation environments.

Abstract

The spread of rumors on social media, particularly during significant events like the US elections and the COVID-19 pandemic, poses a serious threat to social stability and public health. Current rumor detection methods primarily rely on propagation graphs to improve the model performance. However, the effectiveness of these methods is often compromised by noisy and irrelevant structures in the propagation process. To tackle this issue, techniques such as weight adjustment and data augmentation have been proposed. However, they depend heavily on rich original propagation structures, limiting their effectiveness in handling rumors that lack sufficient propagation information, especially in the early stages of dissemination. In this work, we introduce the Key Propagation Graph Generator (KPG), a novel reinforcement learning-based framework, that generates contextually coherent and informative propagation patterns for events with insufficient topology information and identifies significant substructures in events with redundant and noisy propagation structures. KPG comprises two key components: the Candidate Response Generator (CRG) and the Ending Node Selector (ENS). CRG learns latent variable distributions from refined propagation patterns to eliminate noise and generate new candidates for ENS, while ENS identifies the most influential substructures in propagation graphs and provides training data for CRG. Furthermore, we develop an end-to-end framework that utilizes rewards derived from a pre-trained graph neural network to guide the training process. The resulting key propagation graphs are then employed in downstream rumor detection tasks. Extensive experiments conducted on four datasets demonstrate that KPG outperforms current state-of-the-art methods.