# An Audit of Misinformation Filter Bubbles on YouTube: Bubble Bursting and Recent Behavior Changes

arXiv: https://arxiv.org/abs/2203.13769 · HTML: https://mlsanity.com/paper/2203.13769/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 38 references that are in the map.
- First posted on arXiv 25 Mar 2022.

## Builds on (most cited first, 9 of 9)

- [Auditing Radicalization Pathways on YouTube](https://mlsanity.com/paper/1908.08313/) (2019)
- [Measuring Personalization of Web Search](https://mlsanity.com/paper/1706.05011/) (2017)
- [Auditing E-Commerce Platforms for Algorithmically Curated Vaccine Misinformation](https://mlsanity.com/paper/2101.08419/) (2021)
- ["It is just a flu": Assessing the Effect of Watch History on YouTube's Pseudoscientific Video Recommendations](https://mlsanity.com/paper/2010.11638/) (2020)
- [Personality Traits and Echo Chambers on Facebook](https://mlsanity.com/paper/1606.04721/) (2016)
- [Facebook Ads Monitor: An Independent Auditing System for Political Ads on Facebook](https://mlsanity.com/paper/2001.10581/) (2020)
- [Emotional Dynamics in the Age of Misinformation](https://mlsanity.com/paper/1505.08001/) (2015)
- [The Web of False Information: Rumors, Fake News, Hoaxes, Clickbait, and Various Other Shenanigans](https://mlsanity.com/paper/1804.03461/) (2018)
- [A Study of Fake News Reading and Annotating in Social Media Context](https://mlsanity.com/paper/2109.12523/) (2021)

## Built on by (most cited first, 15 of 15)

- [Is ChatGPT Fair for Recommendation? Evaluating Fairness in Large Language Model Recommendation](https://mlsanity.com/paper/2305.07609/) (2023)
- [Human-AI Coevolution](https://mlsanity.com/paper/2306.13723/) (2023)
- [Fairness Definitions in Language Models Explained](https://mlsanity.com/paper/2407.18454/) (2024)
- [Monant Medical Misinformation Dataset: Mapping Articles to Fact-Checked Claims](https://mlsanity.com/paper/2204.12294/) (2022)
- [Bring Your Own Prompts: Use-Case-Specific Bias and Fairness Evaluation for LLMs](https://mlsanity.com/paper/2407.10853/) (2024)
- [Towards Psychologically-Grounded Dynamic Preference Models](https://mlsanity.com/paper/2208.01534/) (2022)
- [Auditing YouTube's Recommendation Algorithm for Misinformation Filter Bubbles](https://mlsanity.com/paper/2210.10085/) (2022)
- [Evaluating Bias in Spoken Dialogue LLMs for Real-World Decisions and Recommendations](https://mlsanity.com/paper/2510.02352/) (2025)
- [You Don't Bring Me Flowers: Mitigating Unwanted Recommendations Through Conformal Risk Control](https://mlsanity.com/paper/2507.16829/) (2025)
- [Explainable AI is Responsible AI: How Explainability Creates Trustworthy and Socially Responsible Artificial Intelligence](https://mlsanity.com/paper/2312.01555/) (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)
- [Uncertainty and Fairness Awareness in LLM-Based Recommendation Systems](https://mlsanity.com/paper/2602.02582/) (2026)
- [The DSA's Blind Spot: Algorithmic Audit of Advertising and Minor Profiling on TikTok](https://mlsanity.com/paper/2603.05653/) (2026)
- [FacetCRS: Multi-Faceted Preference Learning for Pricking Filter Bubbles in Conversational Recommender System](https://mlsanity.com/paper/2609.20175/) (2026)
- [AgentRecommender: LLM Agents Enable Customizable Recommender Systems on the User Side](https://mlsanity.com/paper/2609.31166/) (2026)

## Abstract (arXiv)

The negative effects of misinformation filter bubbles in adaptive systems have been known to researchers for some time. Several studies investigated, most prominently on YouTube, how fast a user can get into a misinformation filter bubble simply by selecting wrong choices from the items offered. Yet, no studies so far have investigated what it takes to burst the bubble, i.e., revert the bubble enclosure. We present a study in which pre-programmed agents (acting as YouTube users) delve into misinformation filter bubbles by watching misinformation promoting content (for various topics). Then, by watching misinformation debunking content, the agents try to burst the bubbles and reach more balanced recommendation mixes. We recorded the search results and recommendations, which the agents encountered, and analyzed them for the presence of misinformation. Our key finding is that bursting of a filter bubble is possible, albeit it manifests differently from topic to topic. Moreover, we observe that filter bubbles do not truly appear in some situations. We also draw a direct comparison with a previous study. Sadly, we did not find much improvements in misinformation occurrences, despite recent pledges by YouTube.