Learning Complex Heterogeneous Multimodal Fake News via Social Latent Network Inference

2025-01-26

AI Summary

To address the challenges of detecting covert and multimodal heterogeneous fake news propagation on social media, this paper proposes an event-driven fake news detection framework that does not rely on explicit retweet chains. Methodologically, it introduces: (1) a novel maximum-likelihood-estimation-based approach to infer latent social networks at the event level, capturing propagation influence; (2) a heterogeneous graph integrating user social attributes and multimodal content, coupled with a personalized node representation learning paradigm; and (3) a self-supervised cross-modal alignment, fusion, and contrastive learning strategy to achieve semantic synergy across modalities. Evaluated on multiple real-world datasets, the proposed method consistently outperforms existing state-of-the-art approaches, achieving significant improvements in both accuracy and robustness.

Technology Category

• Machine Learning: Multimodal Learning
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Computer Vision: Multi-modal Vision

Application Category

• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

With the diversification of online social platforms, news dissemination has become increasingly complex, heterogeneous, and multimodal, making the fake news detection task more challenging and crucial. Previous works mainly focus on obtaining social relationships of news via retweets, limiting the accurate detection when real cascades are inaccessible. Given the proven assessment of the spreading influence of events, this paper proposes a method called HML (Complex Heterogeneous Multimodal Fake News Detection method via Latent Network Inference). Specifically, an improved social latent network inference strategy is designed to estimate the maximum likelihood of news influences under the same event. Meanwhile, a novel heterogeneous graph is built based on social attributes for multimodal news under different events. Further, to better aggregate the relationships among heterogeneous multimodal features, this paper proposes a self-supervised-based multimodal content learning strategy, to enhance, align, fuse and compare heterogeneous modal contents. Based above, a personalized heterogeneous graph representation learning is designed to classify fake news. Extensive experiments demonstrate that the proposed method outperforms the SOTA in real social media news datasets.

Problem

• Fake News Detection
• Social Media
• Information Complexity

Innovation

• HML Method
• Multimodal News Analysis
• Fake News Detection