# RaCMC: Residual-Aware Compensation Network with Multi-Granularity Constraints for Fake News Detection

arXiv: https://arxiv.org/abs/2412.18254 · HTML: https://mlsanity.com/paper/2412.18254/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 45 references that are in the map.
- First posted on arXiv 24 Dec 2024.

## Builds on (most cited first, 2 of 2)

- [Graph Neural Networks with Continual Learning for Fake News Detection from Social Media](https://mlsanity.com/paper/2007.03316/) (2020)
- [Like Article, Like Audience: Enforcing Multimodal Correlations for Disinformation Detection](https://mlsanity.com/paper/2108.13892/) (2021)

## Built on by (most cited first, 5 of 5)

- [Multimodal Inverse Attention Network with Intrinsic Discriminant Feature Exploitation for Fake News Detection](https://mlsanity.com/paper/2502.01699/) (2025)
- [CIEC: Coupling Implicit and Explicit Cues for Multimodal Weakly Supervised Manipulation Localization](https://mlsanity.com/paper/2602.02175/) (2026)
- [LLM-MRD: LLM-Guided Multi-View Reasoning Distillation for Fake News Detection](https://mlsanity.com/paper/2603.19293/) (2026)
- [Bridging Pixels and Words: Mask-Aware Local Semantic Fusion for Multimodal Media Verification](https://mlsanity.com/paper/2603.26052/) (2026)
- [IDO: Incongruity-aware Distribution Optimization for Multimodal Fake News Detection](https://mlsanity.com/paper/2606.03418/) (2026)

## Abstract (arXiv)

Multimodal fake news detection aims to automatically identify real or fake news, thereby mitigating the adverse effects caused by such misinformation. Although prevailing approaches have demonstrated their effectiveness, challenges persist in cross-modal feature fusion and refinement for classification. To address this, we present a residual-aware compensation network with multi-granularity constraints (RaCMC) for fake news detection, that aims to sufficiently interact and fuse cross-modal features while amplifying the differences between real and fake news. First, a multiscale residual-aware compensation module is designed to interact and fuse features at different scales, and ensure both the consistency and exclusivity of feature interaction, thus acquiring high-quality features. Second, a multi-granularity constraints module is implemented to limit the distribution of both the news overall and the image-text pairs within the news, thus amplifying the differences between real and fake news at the news and feature levels. Finally, a dominant feature fusion reasoning module is developed to comprehensively evaluate news authenticity from the perspectives of both consistency and inconsistency. Experiments on three public datasets, including Weibo17, Politifact and GossipCop, reveal the superiority of the proposed method.