# Towards Automatic Detection of Misinformation in Online Medical Videos

arXiv: https://arxiv.org/abs/1909.01543 · HTML: https://mlsanity.com/paper/1909.01543/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Sep 2019.

## Built on by (most cited first, 22 of 22)

- [Multimodal Automated Fact-Checking: A Survey](https://mlsanity.com/paper/2305.13507/) (2023)
- [Misinfo Reaction Frames: Reasoning about Readers' Reactions to News Headlines](https://mlsanity.com/paper/2104.08790/) (2021)
- [FakingRecipe: Detecting Fake News on Short Video Platforms from the Perspective of Creative Process](https://mlsanity.com/paper/2407.16670/) (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)
- [Auditing YouTube's Recommendation Algorithm for Misinformation Filter Bubbles](https://mlsanity.com/paper/2210.10085/) (2022)
- [Has It All Been Solved? Open NLP Research Questions Not Solved by Large Language Models](https://mlsanity.com/paper/2305.12544/) (2023)
- [FactGuard: Agentic Video Misinformation Detection via Reinforcement Learning](https://mlsanity.com/paper/2602.22963/) (2026)
- [Challenges and Considerations with Code-Mixed NLP for Multilingual Societies](https://mlsanity.com/paper/2106.07823/) (2021)
- [ViClaim: A Multilingual Multilabel Dataset for Automatic Claim Detection in Videos](https://mlsanity.com/paper/2504.12882/) (2025)
- [From Manipulation to Mistrust: Explaining Diverse Micro-Video Misinformation for Robust Debunking in the Wild](https://mlsanity.com/paper/2603.25423/) (2026)
- [Identifying Misinformation on YouTube through Transcript Contextual Analysis with Transformer Models](https://mlsanity.com/paper/2307.12155/) (2023)
- [Consistent and Invariant Generalization Learning for Short-video Misinformation Detection](https://mlsanity.com/paper/2507.04061/) (2025)
- [Probing Multimodal Large Language Models on Cognitive Biases in Chinese Short-Video Misinformation](https://mlsanity.com/paper/2601.06600/) (2026)
- [Responsible AI in Healthcare](https://mlsanity.com/paper/2203.03616/) (2022)
- [Algorithmic Behaviors Across Regions: A Geolocation Audit of YouTube Search for COVID-19 Misinformation Between the United States and South Africa](https://mlsanity.com/paper/2409.10168/) (2024)
- [T3SVFND: Towards an Evolving Fake News Detector for Emergencies with Test-time Training on Short Video Platforms](https://mlsanity.com/paper/2507.20286/) (2025)
- [Multimodal Learning for Fake News Detection in Short Videos Using Linguistically Verified Data and Heterogeneous Modality Fusion](https://mlsanity.com/paper/2509.15578/) (2025)
- [Misinformation Span Detection in Videos via Audio Transcripts](https://mlsanity.com/paper/2604.21767/) (2026)
- [Detecting and Explaining Fake News Short Videos with Multimodal Content and Real-World Evidence](https://mlsanity.com/paper/2609.12678/) (2026)
- [Effects of Transcript Compression on LLM-based Medical Misinformation Detection in Japanese YouTube Videos](https://mlsanity.com/paper/2609.30882/) (2026)

## Abstract (arXiv)

Recent years have witnessed a significant increase in the online sharing of medical information, with videos representing a large fraction of such online sources. Previous studies have however shown that more than half of the health-related videos on platforms such as YouTube contain misleading information and biases. Hence, it is crucial to build computational tools that can help evaluate the quality of these videos so that users can obtain accurate information to help inform their decisions. In this study, we focus on the automatic detection of misinformation in YouTube videos. We select prostate cancer videos as our entry point to tackle this problem. The contribution of this paper is twofold. First, we introduce a new dataset consisting of 250 videos related to prostate cancer manually annotated for misinformation. Second, we explore the use of linguistic, acoustic, and user engagement features for the development of classification models to identify misinformation. Using a series of ablation experiments, we show that we can build automatic models with accuracies of up to 74%, corresponding to a 76.5% precision and 73.2% recall for misinformative instances.