CAMFeND: Credibility-Aware Multimodal Fake News Detection with Rotational Attention
In the evolving digital landscape, fake news is a significant challenge, influencing public perception and decision-making. Traditional detection approaches focus on single-modal data or simple multimodal fusion, often overlooking deeper interactions and news credibility. We propose a novel model addressing these limitations by introducing rotational attention and news domain information as a feature. Unlike static attention mechanisms, our rotational attention dynamically shifts query, key, and value roles across text and image inputs, enabling richer cross-modal interaction. Incorporating news domain information further enhances the model’s reliability by associating news posts with top domains extracted from Google search results, reducing false detections. This approach assesses both the content and the broader web context in which the news is discussed. Our model outperforms existing state-of-the-art methods by providing deeper, layered multimodal integration and domain information analysis, resulting in a more robust and adaptive fake news detection system.
- Research Organization:
- University of Arkansas
- Sponsoring Organization:
- Department of Energy
- DOE Contract Number:
- CR0000003
- OSTI ID:
- 2584216
- Country of Publication:
- United States
- Language:
- English
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