MViR: Multi-View Visual-Semantic Representation for Fake News Detection

ACL ARR 2025 February Submission4864 Authors

Abstract: With the rise of online social networks, detecting fake news accurately is essential for a healthy online environment. While existing methods have advanced multimodal fake news detection, they often neglect the multi-view visual-semantic aspects of news, such as different text perspectives of the same image. To address this, we propose a Multi-View Visual-Semantic Representation (MViR) framework. Our approach includes a Multi-View Representation module using pyramid dilated convolution to capture multi-view visual-semantic features, a Multi-View Feature Fusion module to integrate these features with text, and multiple aggregators to extract multi-view semantic cues for detection. Experiments on benchmark datasets demonstrate the superiority of MViR. The codes will be released.

Paper Type: Short

Research Area: Computational Social Science and Cultural Analytics

Research Area Keywords: misinformation detection and analysis

Contribution Types: NLP engineering experiment, Theory

Languages Studied: English, Chinese

Submission Number: 4864