Auditing YouTube’s Recommendation Algorithm for Misinformation Filter Bubbles

Srba, I., Moro, R., Tomlein, M., Pecher, B., Simko, J., Stefancova, E., Kompan, M., Hrckova, A., Podrouzek, J., Gavornik, A., Bielikova, M.

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.

Cite: Srba, I., Moro, R., Tomlein, M., Pecher, B., Simko, J., Stefancova, E., Kompan, M., Hrckova, A., Podrouzek, J., Gavornik, A., Bielikova, M. Auditing YouTube’s Recommendation Algorithm for Misinformation Filter Bubbles. ACM Transactions on Recommender Systems. 1, 1, Article 6 (March 2023), 33 pages.. DOI: 10.1145/3568392


Autori

Ivan Srba
Researcher
Viac
Róbert Móro
Researcher
Viac
Matúš Tomlein
Researcher 10/2020-04/2022
Viac
Branislav Pecher
PhD Student
Viac
Jakub Šimko
Lead and Researcher
Viac
Elena Štefancová
Research Assistant 10/2020 - 08/2021
Viac
Michal Kompan
Lead and Researcher
Viac
Andrea Hrčková
Researcher
Viac
Juraj Podroužek
Lead and Researcher
Viac
Adrián Gavorník
Research Intern
Viac
Maria Bielikova
Lead and Researcher
Viac