# Let the Bullets Fly: Multimodal Fake News Detection with Temporal-Aligned Generative Danmaku

arXiv: https://arxiv.org/abs/2608.22832 · HTML: https://mlsanity.com/paper/2608.22832/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 55 references that are in the map.
- First posted on arXiv 24 Aug 2026.

## Builds on (most cited first, 18 of 18)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks](https://mlsanity.com/paper/2312.14238/) (2023)
- [VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training](https://mlsanity.com/paper/2203.12602/) (2022)
- [Fighting Fake News: Image Splice Detection via Learned Self-Consistency](https://mlsanity.com/paper/1805.04096/) (2018)
- [Generalizing to the Future: Mitigating Entity Bias in Fake News Detection](https://mlsanity.com/paper/2204.09484/) (2022)
- [Exploring the Deceptive Power of LLM-Generated Fake News: A Study of Real-World Detection Challenges](https://mlsanity.com/paper/2403.18249/) (2024)
- [FakingRecipe: Detecting Fake News on Short Video Platforms from the Perspective of Creative Process](https://mlsanity.com/paper/2407.16670/) (2024)
- [Leveraging the Crowd to Detect and Reduce the Spread of Fake News and Misinformation](https://mlsanity.com/paper/1711.09918/) (2017)
- [Exploiting User Comments for Early Detection of Fake News Prior to Users' Commenting](https://mlsanity.com/paper/2310.10429/) (2023)
- [ViCo: Engaging Video Comment Generation with Human Preference Rewards](https://mlsanity.com/paper/2308.11171/) (2023)
- [Out-of-distribution Evidence-aware Fake News Detection via Dual Adversarial Debiasing](https://mlsanity.com/paper/2304.12888/) (2023)
- [Is LLMs Hallucination Usable? LLM-based Negative Reasoning for Fake News Detection](https://mlsanity.com/paper/2503.09153/) (2025)
- [Synergizing LLMs with Global Label Propagation for Multimodal Fake News Detection](https://mlsanity.com/paper/2506.00488/) (2025)
- [Learning Complex Heterogeneous Multimodal Fake News via Social Latent Network Inference](https://mlsanity.com/paper/2501.15508/) (2025)
- [Beyond Detection: Exploring Evidence-based Multi-Agent Debate for Misinformation Intervention and Persuasion](https://mlsanity.com/paper/2511.07267/) (2025)
- [Deconfounded Reasoning for Multimodal Fake News Detection via Causal Intervention](https://mlsanity.com/paper/2504.09163/) (2025)
- [Semantic Frame Aggregation-based Transformer for Live Video Comment Generation](https://mlsanity.com/paper/2510.26978/) (2025)

## Abstract (arXiv)

The social interactions among crowds via textitDanmaku (a.k.a., bullet comments) on modern multimedia platforms can facilitate both viewpoint conflicts and consensus, providing fine-grained discriminative social signals that can benefit fake news detection. However, the inherent accumulation latency of textitDanmaku in real-world scenarios violates the real-time necessity of fake news detection, making the studies of textitDanmaku-related fake news detection underexplored. To break this violation, we simulate this temporal-aware user interactive process by proposing a novel temporal Generative danmaku framework, called Genda, which consists of: (1) a textitDanmaku Trigger for predicting the timing and intensity of user reactions; and (2) a textitDanmaku Generator for synthesizing corresponding semantic and emotional expressions, thereby mutually constructing a temporally aligned and human-like pseudo textitDanmaku streams. To make the generated textitDanmaku useful for identifying fake news videos, we further design a textitDanmaku-guided Temporal Multimodal fake news detection model - DM-FEND, which enables fine-grained multimodal interactions among video, audio, text, and textitDanmaku, enhancing dynamic modalities alignment and semantic noise inhibition. The experimental results demonstrate that DM-FEND consistently outperforms state-of-the-art baselines across both Chinese (FakeSV) and English (FakeTT) benchmarks. Further ablations validate the crucial role of temporal textitDanmaku modeling in enhancing robustness and discriminative capability. Finally, this study offers a bright and robust solution for multimodal fake news detection in modern social interactive fashions by bridging the temporal inconsistency between news and user behaviors.