mlsanityData to 14 Oct 2026

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

arXiv 2203.13769 ↗First posted 25 Mar 2022cs.IR cs.AIcited by 15

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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.

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

9 of 38 references.

References · 9

  1. 2019Auditing Radicalization Pathways on YouTubecited by 53
  2. 2017Measuring Personalization of Web Searchcited by 20
  3. 2021Auditing E-Commerce Platforms for Algorithmically Curated Vaccine Misinformationcited by 11
  4. 2020"It is just a flu": Assessing the Effect of Watch History on YouTube's Pseudoscientific Video Recommendationscited by 10
  5. 2016Personality Traits and Echo Chambers on Facebookcited by 9
  6. 2020Facebook Ads Monitor: An Independent Auditing System for Political Ads on Facebookcited by 9
  7. 2015Emotional Dynamics in the Age of Misinformationcited by 8
  8. 2018The Web of False Information: Rumors, Fake News, Hoaxes, Clickbait, and Various Other Shenaniganscited by 8
  9. 2021A Study of Fake News Reading and Annotating in Social Media Contextcited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 3; 2023: 3; 2024: 3; 2025: 2; 2026: 4.

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  1. 2023Is ChatGPT Fair for Recommendation? Evaluating Fairness in Large Language Model Recommendationcited by 114
  2. 2023Human-AI Coevolutioncited by 27
  3. 2024Fairness Definitions in Language Models Explainedcited by 11
  4. 2022Monant Medical Misinformation Dataset: Mapping Articles to Fact-Checked Claimscited by 10
  5. 2024Bring Your Own Prompts: Use-Case-Specific Bias and Fairness Evaluation for LLMscited by 9
  6. 2022Towards Psychologically-Grounded Dynamic Preference Modelscited by 8
  7. 2022Auditing YouTube's Recommendation Algorithm for Misinformation Filter Bubblescited by 8
  8. 2025Evaluating Bias in Spoken Dialogue LLMs for Real-World Decisions and Recommendationscited by 7
  9. 2025You Don't Bring Me Flowers: Mitigating Unwanted Recommendations Through Conformal Risk Controlcited by 3
  10. 2023Explainable AI is Responsible AI: How Explainability Creates Trustworthy and Socially Responsible Artificial Intelligencecited by 2
  11. 2024Algorithmic Behaviors Across Regions: A Geolocation Audit of YouTube Search for COVID-19 Misinformation Between the United States and South Africacited by 1
  12. 2026Uncertainty and Fairness Awareness in LLM-Based Recommendation Systemscited by 1
3 more
  1. 2026The DSA's Blind Spot: Algorithmic Audit of Advertising and Minor Profiling on TikTokcited by 1
  2. 2026FacetCRS: Multi-Faceted Preference Learning for Pricking Filter Bubbles in Conversational Recommender Systemcited by 0
  3. 2026AgentRecommender: LLM Agents Enable Customizable Recommender Systems on the User Sidecited by 0