Rumor Detection on Social Media with Temporal Propagation Structure Optimization

TL;DR

This paper introduces a novel rumor detection method on social media that incorporates temporal propagation structures using weighted trees and structural entropy, enhancing accuracy over traditional content-based approaches.

Contribution

It proposes a new approach combining temporal weighted trees, structural entropy, and recursive neural networks for improved rumor detection.

Findings

Outperforms existing methods on benchmark datasets

Effectively captures temporal dynamics of rumor spread

Reduces noise in propagation structure modeling

Abstract

Traditional methods for detecting rumors on social media primarily focus on analyzing textual content, often struggling to capture the complexity of online interactions. Recent research has shifted towards leveraging graph neural networks to model the hierarchical conversation structure that emerges during rumor propagation. However, these methods tend to overlook the temporal aspect of rumor propagation and may disregard potential noise within the propagation structure. In this paper, we propose a novel approach that incorporates temporal information by constructing a weighted propagation tree, where the weight of each edge represents the time interval between connected posts. Drawing upon the theory of structural entropy, we transform this tree into a coding tree. This transformation aims to preserve the essential structure of rumor propagation while reducing noise. Finally, we introduce a recursive neural network to learn from the coding tree for rumor veracity prediction. Experimental results on two common datasets demonstrate the superiority of our approach.