Propagation Structure-Semantic Transfer Learning for Robust Fake News Detection

TL;DR

This paper introduces PSS-TL, a transfer learning framework with dual teachers and knowledge distillation to improve fake news detection robustness against noisy content and propagation signals.

Contribution

It proposes a novel teacher-student transfer learning approach with dual teachers and multi-channel knowledge distillation for robust fake news detection.

Findings

The method outperforms existing approaches on real-world datasets.

It effectively reduces the impact of semantic and structural noise.

Experimental results demonstrate improved robustness and accuracy.

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.