# "It is just a flu": Assessing the Effect of Watch History on YouTube's Pseudoscientific Video Recommendations

arXiv: https://arxiv.org/abs/2010.11638 · HTML: https://mlsanity.com/paper/2010.11638/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Oct 2020.

## Built on by (most cited first, 10 of 10)

- [A Comprehensive Survey on Trustworthy Recommender Systems](https://mlsanity.com/paper/2209.10117/) (2022)
- [LLMRec: Benchmarking Large Language Models on Recommendation Task](https://mlsanity.com/paper/2308.12241/) (2023)
- [An Audit of Misinformation Filter Bubbles on YouTube: Bubble Bursting and Recent Behavior Changes](https://mlsanity.com/paper/2203.13769/) (2022)
- [Auditing YouTube's Recommendation Algorithm for Misinformation Filter Bubbles](https://mlsanity.com/paper/2210.10085/) (2022)
- [ViClaim: A Multilingual Multilabel Dataset for Automatic Claim Detection in Videos](https://mlsanity.com/paper/2504.12882/) (2025)
- [Identifying Misinformation on YouTube through Transcript Contextual Analysis with Transformer Models](https://mlsanity.com/paper/2307.12155/) (2023)
- [Reducing Exposure to Harmful Content via Graph Rewiring](https://mlsanity.com/paper/2306.07930/) (2023)
- [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)
- [Prompt-Based LLMs for Position Bias-Aware Reranking in Personalized Recommendations](https://mlsanity.com/paper/2505.04948/) (2025)
- [Privacy-Preserving Online Content Moderation: A Federated Learning Use Case](https://mlsanity.com/paper/2209.11843/) (2022)

## Abstract (arXiv)

The role played by YouTube's recommendation algorithm in unwittingly promoting misinformation and conspiracy theories is not entirely understood. Yet, this can have dire real-world consequences, especially when pseudoscientific content is promoted to users at critical times, such as the COVID-19 pandemic. In this paper, we set out to characterize and detect pseudoscientific misinformation on YouTube. We collect 6.6K videos related to COVID-19, the Flat Earth theory, as well as the anti-vaccination and anti-mask movements. Using crowdsourcing, we annotate them as pseudoscience, legitimate science, or irrelevant and train a deep learning classifier to detect pseudoscientific videos with an accuracy of 0.79. We quantify user exposure to this content on various parts of the platform and how this exposure changes based on the user's watch history. We find that YouTube suggests more pseudoscientific content regarding traditional pseudoscientific topics (e.g., flat earth, anti-vaccination) than for emerging ones (like COVID-19). At the same time, these recommendations are more common on the search results page than on a user's homepage or in the recommendation section when actively watching videos. Finally, we shed light on how a user's watch history substantially affects the type of recommended videos.