# Disentangling Fact from Sentiment: A Dynamic Conflict-Consensus Framework for Multimodal Fake News Detection

arXiv: https://arxiv.org/abs/2512.20670 · HTML: https://mlsanity.com/paper/2512.20670/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 19 references that are in the map.
- First posted on arXiv 19 Dec 2025.

## Builds on (most cited first, 14 of 14)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Chinese CLIP: Contrastive Vision-Language Pretraining in Chinese](https://mlsanity.com/paper/2211.01335/) (2022)
- [Multimodal Fake News Detection via CLIP-Guided Learning](https://mlsanity.com/paper/2205.14304/) (2022)
- [Bootstrapping Multi-view Representations for Fake News Detection](https://mlsanity.com/paper/2206.05741/) (2022)
- [Modality Interactive Mixture-of-Experts for Fake News Detection](https://mlsanity.com/paper/2501.12431/) (2025)
- [Synergizing LLMs with Global Label Propagation for Multimodal Fake News Detection](https://mlsanity.com/paper/2506.00488/) (2025)
- [KEN: Knowledge Augmentation and Emotion Guidance Network for Multimodal Fake News Detection](https://mlsanity.com/paper/2507.09647/) (2025)
- [MVAN: Multi-View Attention Networks for Fake News Detection on Social Media](https://mlsanity.com/paper/2506.01627/) (2025)
- [Prompt-Induced Linguistic Fingerprints for LLM-Generated Fake News Detection](https://mlsanity.com/paper/2508.12632/) (2025)
- [SEER: Semantic Enhancement and Emotional Reasoning Network for Multimodal Fake News Detection](https://mlsanity.com/paper/2507.13415/) (2025)
- [Bridging Thoughts and Words: Graph-Based Intent-Semantic Joint Learning for Fake News Detection](https://mlsanity.com/paper/2509.01660/) (2025)

## Built on by (most cited first, 1 of 1)

- [MOMENTA: Mixture-of-Experts Over Multimodal Embeddings with Neural Temporal Aggregation for Misinformation Detection](https://mlsanity.com/paper/2604.16172/) (2026)

## Abstract (arXiv)

Prevalent multimodal fake news detection relies on consistency-based fusion, yet this paradigm fundamentally misinterprets critical cross-modal discrepancies as noise, leading to over-smoothing, which dilutes critical evidence of fabrication. Mainstream consistency-based fusion inherently minimizes feature discrepancies to align modalities, yet this approach fundamentally fails because it inadvertently smoothes out the subtle cross-modal contradictions that serve as the primary evidence of fabrication. To address this, we propose the Dynamic Conflict-Consensus Framework (DCCF), an inconsistency-seeking paradigm designed to amplify rather than suppress contradictions. First, DCCF decouples inputs into independent Fact and Sentiment spaces to distinguish objective mismatches from emotional dissonance. Second, we employ physics-inspired feature dynamics to iteratively polarize these representations, actively extracting maximally informative conflicts. Finally, a conflict-consensus mechanism standardizes these local discrepancies against the global context for robust deliberative judgment.Extensive experiments conducted on three real world datasets demonstrate that DCCF consistently outperforms state-of-the-art baselines, achieving an average accuracy improvement of 3.52%.