MVAN: Multi-View Attention Networks for Fake News Detection on Social Media

2025-06-02

IEEE Access

AI Summary

To address the challenge of detecting fake news in real-world social scenarios—where only source tweets (short texts) and retweet user structures (without comments) are available—this paper proposes a Multi-View Attention Network (MVAN), the first framework to jointly model semantic attention over short text and structural attention over propagation graphs. MVAN employs dual-path self-attention mechanisms: one to identify salient lexical cues in the source tweet, and another to detect suspicious retweeters based on propagation topology, enabling end-to-end co-learning of semantic and diffusion patterns. The model achieves both high detection accuracy and intrinsic interpretability: it outperforms state-of-the-art methods by an average of 2.5% in accuracy on two real-world datasets, while generating traceable, semantically grounded explanations for its predictions.

Technology Category

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

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Fake news on social media is a widespread and serious problem in today’s society. Existing fake news detection methods focus on finding clues from Long text content, such as original news articles and user comments. This paper solves the problem of fake news detection in more realistic scenarios. Only source shot-text tweet and its retweet users are provided without user comments. We develop a novel neural network based model, Multi-View Attention Networks (MVAN) to detect fake news and provide explanations on social media. The MVAN model includes text semantic attention and propagation structure attention, which ensures that our model can capture information and clues both of source tweet content and propagation structure. In addition, the two attention mechanisms in the model can find key clue words in fake news texts and suspicious users in the propagation structure. We conduct experiments on two real-world datasets, and the results demonstrate that MVAN can significantly outperform state-of-the-art methods by 2.5% in accuracy on average, and produce a reasonable explanation.

Problem

• Detects fake news using short-text tweets without user comments
• Identifies key clue words and suspicious users in propagation
• Improves accuracy by 2.5% over state-of-the-art methods

Innovation

• Multi-View Attention Networks for fake news detection
• Combines text semantic and propagation structure attention
• Identifies key clue words and suspicious users