# FakingRecipe: Detecting Fake News on Short Video Platforms from the Perspective of Creative Process

arXiv: https://arxiv.org/abs/2407.16670 · HTML: https://mlsanity.com/paper/2407.16670/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 58 references that are in the map.
- First posted on arXiv 23 Jul 2024.

## Builds on (most cited first, 24 of 24)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks](https://mlsanity.com/paper/1908.02265/) (2019)
- [The Dawn of LMMs: Preliminary Explorations with GPT-4V(ision)](https://mlsanity.com/paper/2309.17421/) (2023)
- [SpeechBrain: A General-Purpose Speech Toolkit](https://mlsanity.com/paper/2106.04624/) (2021)
- [Fake News Detection on Social Media: A Data Mining Perspective](https://mlsanity.com/paper/1708.01967/) (2017)
- [Character Region Awareness for Text Detection](https://mlsanity.com/paper/1904.01941/) (2019)
- [TransNet V2: An effective deep network architecture for fast shot transition detection](https://mlsanity.com/paper/2008.04838/) (2020)
- [Clicks can be Cheating: Counterfactual Recommendation for Mitigating Clickbait Issue](https://mlsanity.com/paper/2009.09945/) (2020)
- [Improving Fake News Detection by Using an Entity-enhanced Framework to Fuse Diverse Multimodal Clues](https://mlsanity.com/paper/2108.10509/) (2021)
- [FakeSV: A Multimodal Benchmark with Rich Social Context for Fake News Detection on Short Video Platforms](https://mlsanity.com/paper/2211.10973/) (2022)
- [Learning Hierarchical Discourse-level Structure for Fake News Detection](https://mlsanity.com/paper/1903.07389/) (2019)
- [Integrating Pattern- and Fact-based Fake News Detection via Model Preference Learning](https://mlsanity.com/paper/2109.11333/) (2021)
- [Bootstrapping Multi-view Representations for Fake News Detection](https://mlsanity.com/paper/2206.05741/) (2022)
- [Towards Automatic Detection of Misinformation in Online Medical Videos](https://mlsanity.com/paper/1909.01543/) (2019)
- [COVID-VTS: Fact Extraction and Verification on Short Video Platforms](https://mlsanity.com/paper/2302.07919/) (2023)
- [Two Heads Are Better Than One: Improving Fake News Video Detection by Correlating with Neighbors](https://mlsanity.com/paper/2306.05241/) (2023)
- [Combating Online Misinformation Videos: Characterization, Detection, and Future Directions](https://mlsanity.com/paper/2302.03242/) (2023)
- [Misleading Metadata Detection on YouTube](https://mlsanity.com/paper/1901.08759/) (2019)
- [Misinformation Detection on YouTube Using Video Captions](https://mlsanity.com/paper/2107.00941/) (2021)
- [Detecting misinformation through Framing Theory: the Frame Element-based Model](https://mlsanity.com/paper/2402.15525/) (2024)

## Built on by (most cited first, 25 of 25)

- [Fact-R1: Towards Explainable Video Misinformation Detection with Deep Reasoning](https://mlsanity.com/paper/2505.16836/) (2025)
- [Truth over Tricks: Measuring and Mitigating Shortcut Learning in Misinformation Detection](https://mlsanity.com/paper/2506.02350/) (2025)
- [The Paradigm Shift: A Comprehensive Survey on Large Vision Language Models for Multimodal Fake News Detection](https://mlsanity.com/paper/2601.15316/) (2026)
- [FactGuard: Agentic Video Misinformation Detection via Reinforcement Learning](https://mlsanity.com/paper/2602.22963/) (2026)
- [From Manipulation to Mistrust: Explaining Diverse Micro-Video Misinformation for Robust Debunking in the Wild](https://mlsanity.com/paper/2603.25423/) (2026)
- [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)
- [Probing Multimodal Large Language Models on Cognitive Biases in Chinese Short-Video Misinformation](https://mlsanity.com/paper/2601.06600/) (2026)
- [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)
- [ShortCheck: Checkworthiness Detection of Multilingual Short-Form Videos](https://mlsanity.com/paper/2509.20467/) (2025)
- [Unveiling Modality Bias: Automated Sample-Specific Analysis for Multimodal Misinformation Benchmarks](https://mlsanity.com/paper/2511.05883/) (2025)
- [Agentic Multi-Persona Framework for Evidence-Aware Fake News Detection](https://mlsanity.com/paper/2512.21039/) (2025)
- [Nip Rumors in the Bud: Retrieval-Guided Topic-Level Adaptation for Test-Time Fake News Video Detection](https://mlsanity.com/paper/2601.11981/) (2026)
- [Exposing Cross-Modal Consistency for Fake News Detection in Short-Form Videos](https://mlsanity.com/paper/2603.14992/) (2026)
- [From Cheap Fakes to Pure Synthesis: Addressing the New Era of T2V Fake News Videos](https://mlsanity.com/paper/2608.06732/) (2026)
- [Multimodal Learning for Fake News Detection in Short Videos Using Linguistically Verified Data and Heterogeneous Modality Fusion](https://mlsanity.com/paper/2509.15578/) (2025)
- [Harmful Visual Content Manipulation Matters in Misinformation Detection Under Multimedia Scenarios](https://mlsanity.com/paper/2603.21054/) (2026)
- [OmniVL-Guard Pro: A Tool-Augmented Agent for Omnibus Vision-Language Forensics](https://mlsanity.com/paper/2605.16962/) (2026)
- [Sparse Evidence Can Suffice: Agentic Evidence Seeking for Multimodal Video Misinformation Detection](https://mlsanity.com/paper/2607.18080/) (2026)
- [HyperClaim: Fine-Grained Cross-Modal Hypergraph Reasoning for Video Misinformation Detection](https://mlsanity.com/paper/2607.28375/) (2026)
- [Let the Bullets Fly: Multimodal Fake News Detection with Temporal-Aligned Generative Danmaku](https://mlsanity.com/paper/2608.22832/) (2026)
- [Detecting and Explaining Fake News Short Videos with Multimodal Content and Real-World Evidence](https://mlsanity.com/paper/2609.12678/) (2026)

## Abstract (arXiv)

As short-form video-sharing platforms become a significant channel for news consumption, fake news in short videos has emerged as a serious threat in the online information ecosystem, making developing detection methods for this new scenario an urgent need. Compared with that in text and image formats, fake news on short video platforms contains rich but heterogeneous information in various modalities, posing a challenge to effective feature utilization. Unlike existing works mostly focusing on analyzing what is presented, we introduce a novel perspective that considers how it might be created. Through the lens of the creative process behind news video production, our empirical analysis uncovers the unique characteristics of fake news videos in material selection and editing. Based on the obtained insights, we design FakingRecipe, a creative process-aware model for detecting fake news short videos. It captures the fake news preferences in material selection from sentimental and semantic aspects and considers the traits of material editing from spatial and temporal aspects. To improve evaluation comprehensiveness, we first construct FakeTT, an English dataset for this task, and conduct experiments on both FakeTT and the existing Chinese FakeSV dataset. The results show FakingRecipe's superiority in detecting fake news on short video platforms.