# FakeSV: A Multimodal Benchmark with Rich Social Context for Fake News Detection on Short Video Platforms

arXiv: https://arxiv.org/abs/2211.10973 · HTML: https://mlsanity.com/paper/2211.10973/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Nov 2022.

## Built on by (most cited first, 30 of 41)

- [SNIFFER: Multimodal Large Language Model for Explainable Out-of-Context Misinformation Detection](https://mlsanity.com/paper/2403.03170/) (2024)
- [Multimodal Automated Fact-Checking: A Survey](https://mlsanity.com/paper/2305.13507/) (2023)
- [SafeWatch: An Efficient Safety-Policy Following Video Guardrail Model with Transparent Explanations](https://mlsanity.com/paper/2412.06878/) (2024)
- [FakingRecipe: Detecting Fake News on Short Video Platforms from the Perspective of Creative Process](https://mlsanity.com/paper/2407.16670/) (2024)
- [Emotion Detection for Misinformation: A Review](https://mlsanity.com/paper/2311.00671/) (2023)
- [Let Silence Speak: Enhancing Fake News Detection with Generated Comments from Large Language Models](https://mlsanity.com/paper/2405.16631/) (2024)
- [Combating Online Misinformation Videos: Characterization, Detection, and Future Directions](https://mlsanity.com/paper/2302.03242/) (2023)
- [Fact-R1: Towards Explainable Video Misinformation Detection with Deep Reasoning](https://mlsanity.com/paper/2505.16836/) (2025)
- [Detecting Misinformation in Multimedia Content through Cross-Modal Entity Consistency: A Dual Learning Approach](https://mlsanity.com/paper/2409.00022/) (2024)
- [DocMSU: A Comprehensive Benchmark for Document-level Multimodal Sarcasm Understanding](https://mlsanity.com/paper/2312.16023/) (2023)
- [CMIE: Combining MLLM Insights with External Evidence for Explainable Out-of-Context Misinformation Detection](https://mlsanity.com/paper/2505.23449/) (2025)
- [The Paradigm Shift: A Comprehensive Survey on Large Vision Language Models for Multimodal Fake News Detection](https://mlsanity.com/paper/2601.15316/) (2026)
- [Not all Fake News is Written: A Dataset and Analysis of Misleading Video Headlines](https://mlsanity.com/paper/2310.13859/) (2023)
- [FactGuard: Agentic Video Misinformation Detection via Reinforcement Learning](https://mlsanity.com/paper/2602.22963/) (2026)
- [A Revisit of Fake News Dataset with Augmented Fact-checking by ChatGPT](https://mlsanity.com/paper/2312.11870/) (2023)
- [CPFD: Confidence-aware Privileged Feature Distillation for Short Video Classification](https://mlsanity.com/paper/2410.03038/) (2024)
- [Looking Beyond the Obvious: A Survey on Abstract Concept Recognition for Video Understanding](https://mlsanity.com/paper/2508.20765/) (2025)
- [OutSafe-Bench: A Benchmark for Multimodal Offensive Content Detection in Large Language Models](https://mlsanity.com/paper/2511.10287/) (2025)
- [BLM-Guard: Explainable Multimodal Ad Moderation with Chain-of-Thought and Policy-Aligned Rewards](https://mlsanity.com/paper/2602.18193/) (2026)
- [From Manipulation to Mistrust: Explaining Diverse Micro-Video Misinformation for Robust Debunking in the Wild](https://mlsanity.com/paper/2603.25423/) (2026)
- [Emulating Reader Behaviors for Fake News Detection](https://mlsanity.com/paper/2306.15231/) (2023)
- [Deconfounded Reasoning for Multimodal Fake News Detection via Causal Intervention](https://mlsanity.com/paper/2504.09163/) (2025)
- [Consistent and Invariant Generalization Learning for Short-video Misinformation Detection](https://mlsanity.com/paper/2507.04061/) (2025)
- [A New Dataset and Benchmark for Grounding Multimodal Misinformation](https://mlsanity.com/paper/2509.08008/) (2025)
- [Enhancing Fake News Video Detection via LLM-Driven Creative Process Simulation](https://mlsanity.com/paper/2510.04024/) (2025)
- [Official-NV: An LLM-Generated News Video Dataset for Multimodal Fake News Detection](https://mlsanity.com/paper/2407.19493/) (2024)
- [Multimodal Fake News Video Explanation: Dataset, Analysis and Evaluation](https://mlsanity.com/paper/2501.08514/) (2025)
- [FMNV: A Dataset of Media-Published News Videos for Fake News Detection](https://mlsanity.com/paper/2504.07687/) (2025)
- [Exploring Modality Disruption in Multimodal Fake News Detection](https://mlsanity.com/paper/2504.09154/) (2025)
- [Unveiling Modality Bias: Automated Sample-Specific Analysis for Multimodal Misinformation Benchmarks](https://mlsanity.com/paper/2511.05883/) (2025)

## Abstract (arXiv)

Short video platforms have become an important channel for news sharing, but also a new breeding ground for fake news. To mitigate this problem, research of fake news video detection has recently received a lot of attention. Existing works face two roadblocks: the scarcity of comprehensive and largescale datasets and insufficient utilization of multimodal information. Therefore, in this paper, we construct the largest Chinese short video dataset about fake news named FakeSV, which includes news content, user comments, and publisher profiles simultaneously. To understand the characteristics of fake news videos, we conduct exploratory analysis of FakeSV from different perspectives. Moreover, we provide a new multimodal detection model named SV-FEND, which exploits the cross-modal correlations to select the most informative features and utilizes the social context information for detection. Extensive experiments evaluate the superiority of the proposed method and provide detailed comparisons of different methods and modalities for future works.