Auditing YouTube's Recommendation Algorithm for Misinformation Filter Bubbles

Abstract

In this paper, we present results of an auditing study performed over YouTube aimed at investigating how fast a user can get into a misinformation filter bubble, but also what it takes to "burst the bubble", i.e., revert the bubble enclosure. We employ a sock puppet audit methodology, in which pre-programmed agents (acting as YouTube users) delve into misinformation filter bubbles by watching misinformation promoting content. Then they try to burst the bubbles and reach more balanced recommendations by watching misinformation debunking content. We record search results, home page results, and recommendations for the watched videos. Overall, we recorded 17,405 unique videos, out of which we manually annotated 2,914 for the presence of misinformation. The labeled data was used to train a machine learning model classifying videos into three classes (promoting, debunking, neutral) with the accuracy of 0.82. We use the trained model to classify the remaining videos that would not be feasible to annotate manually. Using both the manually and automatically annotated data, we observe the misinformation bubble dynamics for a range of audited topics. Our key finding is that even though filter bubbles do not appear in some situations, when they do, it is possible to burst them by watching misinformation debunking content (albeit it manifests differently from topic to topic). We also observe a sudden decrease of misinformation filter bubble effect when misinformation debunking videos are watched after misinformation promoting videos, suggesting a strong contextuality of recommendations. Finally, when comparing our results with a previous similar study, we do not observe significant improvements in the overall quantity of recommended misinformation content.

Just accepted to ACM Transactions on Recommender Systems (ACM TORS). arXiv admin note: substantial text overlap with arXiv:2203.13769

References in corpus (8)

• Everyday algorithm auditing: Understanding the power of everyday users in surfacing harmful algorithmic behaviors
• Degenerate Feedback Loops in Recommender Systems
• Facebook Ads Monitor: An Independent Auditing System for Political Ads on Facebook
• An Audit of Misinformation Filter Bubbles on YouTube: Bubble Bursting and Recent Behavior Changes
• Auditing E-Commerce Platforms for Algorithmically Curated Vaccine Misinformation
• Conspiracy Brokers: Understanding the Monetization of YouTube Conspiracy Theories
• YouTube, The Great Radicalizer? Auditing and Mitigating Ideological Biases in YouTube Recommendations
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• Understanding the Influence of Data Characteristics on the Performance of Point-of-Interest Recommendation Algorithms
• Prompt-to-Slate: Diffusion Models for Prompt-Conditioned Slate Generation
• Revisiting Algorithmic Audits of TikTok: Poor Reproducibility and Short-term Validity of Findings
• Algorithmic Audit of Personalisation Drift in Polarising Topics on TikTok
• How Does Empowering Users with Greater System Control Affect News Filter Bubbles?