MPPFND: A Dataset and Analysis of Detecting Fake News with Multi-Platform Propagation

TL;DR

This paper introduces the MPPFND dataset and a multi-platform detection model that leverages cross-platform propagation differences to improve fake news detection accuracy.

Contribution

The paper presents a new dataset capturing multi-platform propagation and a novel graph neural network-based model for cross-platform fake news detection.

Findings

Cross-platform propagation features enhance detection accuracy

Distinct social context features are observed across platforms

The proposed model outperforms single-platform detection methods

Abstract

Fake news spreads widely on social media, leading to numerous negative effects. Most existing detection algorithms focus on analyzing news content and social context to detect fake news. However, these approaches typically detect fake news based on specific platforms, ignoring differences in propagation characteristics across platforms. In this paper, we introduce the MPPFND dataset, which captures propagation structures across multiple platforms. We also describe the commenting and propagation characteristics of different platforms to show that their social contexts have distinct features. We propose a multi-platform fake news detection model (APSL) that uses graph neural networks to extract social context features from various platforms. Experiments show that accounting for cross-platform propagation differences improves fake news detection performance.