MPPFND: A Dataset and Analysis of Detecting Fake News with Multi-Platform Propagation
Congyuan Zhao, Lingwei Wei, Ziming Qin, Wei Zhou, Yunya Song, Songlin Hu
TL;DR
This work addresses the gap in fake news detection by analyzing propagation across multiple platforms rather than single-platform signals. It provides the MPPFND dataset, capturing cross-platform propagation across YouTube, X, and Reddit, and analyzes platform-specific differences in propagation, comments, and emotions. The authors introduce Adaptive Propagation Structure Learning Network (APSL), which uses platform adapters, per-platform GNNs, and an attention-based fusion plus platform-aware contrastive learning to leverage cross-platform signals for detection. Experiments show that multi-platform propagation data improve detection performance, validating the importance of platform-specific modeling for robust fake news detection in real-world, multi-platform ecosystems.
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.