Propagation Structure-Semantic Transfer Learning for Robust Fake News Detection

2026-04-26

AI Summary

This work addresses the challenge of mutual interference between semantic noise in content and structural noise in propagation patterns in fake news detection. To tackle this issue, the authors propose a teacher–student architecture-based transfer learning framework that jointly leverages propagation structure and semantic information. Specifically, two teacher models independently distill semantic knowledge from noisy textual content and structural knowledge from noisy propagation graphs, respectively. A multi-channel knowledge distillation mechanism is then designed to guide the student model in effectively fusing these complementary signals while decoupling their respective noise sources. Extensive experiments on two real-world datasets demonstrate that the proposed method achieves significant improvements in both detection accuracy and robustness compared to existing approaches.

Technology Category

• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Machine Learning: Structured Learning
• Application Domains: Misinformation & Fake News

Application Category

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

Abstract

Fake news generally refers to false information that is spread deliberately to deceive people, which has detrimental social effects. Existing fake news detection methods primarily learn the semantic features from news content or integrate structural features from propagation. However, in practical scenarios, due to the semantic ambiguity of informal language and unreliable user interactive behaviors on social media, there are inherent semantic and structural noises in news content and propagation. Although some recent works consider the effect of irrelevant user interactions in a hybrid-modeling way, they still suffer from the mutual interference between structural noise and semantic noise, leading to limited performance for robust detection. To alleviate this issue, this paper proposes a novel Propagation Structure-Semantic Transfer Learning framework (PSS-TL) for robust fake news detection under a teacher-student architecture. Specifically, we design dual teacher models to learn semantics knowledge and structure knowledge from noisy news content and propagation structure independently. Besides, we design a Multi-channel Knowledge Distillation (MKD) loss to enable the student model to acquire specialized knowledge from the teacher models, thereby avoiding mutual interference. Extensive experiments on two real-world datasets validate the effectiveness and robustness of our method.

Problem

• fake news detection
• semantic noise
• structural noise
• noise interference
• robustness

Innovation

• Propagation Structure
• Semantic Transfer Learning
• Knowledge Distillation
• Fake News Detection
• Teacher-Student Architecture