research-article

Auditing YouTube’s Recommendation Algorithm for Misinformation Filter Bubbles

Authors:

Branislav Pecher,

Elena Stefancova,

Andrea Hrckova,

Juraj Podrouzek,

Adrian Gavornik,

Maria BielikovaAuthors Info & Claims

ACM Transactions on Recommender Systems, Volume 1, Issue 1

Article No.: 6, Pages 1 - 33

https://doi.org/10.1145/3568392

Published: 27 January 2023 Publication History

Abstract

In this article, 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.

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Auditing YouTube’s Recommendation Algorithm for Misinformation Filter Bubbles

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cover image ACM Transactions on Recommender Systems

ACM Transactions on Recommender Systems Volume 1, Issue 1

March 2023

163 pages

EISSN:2770-6699

DOI:10.1145/3581755

Editors:
Li Chen
Hong Kong Baptist University, China
,
Dietmar Jannach
University of Klagenfurt, Austria

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Publication History

Published: 27 January 2023

Online AM: 26 October 2022

Accepted: 05 October 2022

Revised: 22 August 2022

Received: 31 March 2022

Published in TORS Volume 1, Issue 1

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Ministry of Education, Science, Research and Sport of the Slovak Republic
Central European Digital Media Observatory (CEDMO)
European Union
EU Horizon 2020
Horizon Europe, GA

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https://dl.acm.org/doi/10.1145/3648188.3675128
Duskin K(2024)Stemming the Tide of Problematic Information in Online Environments: Assessing Interventions and Identifying Opportunities for InterruptionCompanion Publication of the 16th ACM Web Science Conference10.1145/3630744.3658615(37-41)Online publication date: 21-May-2024
https://dl.acm.org/doi/10.1145/3630744.3658615
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Hilbert MThakur AFlores PZhang XBhan JBernhard PJi F(2024)8–10% of algorithmic recommendations are ‘bad’, but… an exploratory risk-utility meta-analysis and its regulatory implicationsInternational Journal of Information Management: The Journal for Information Professionals10.1016/j.ijinfomgt.2023.10274375:COnline publication date: 1-Apr-2024
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Ioannou AIoannou NKleanthous S(2023)Cyclops: Looking beyond the single perspective in information access systemsAdjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization10.1145/3563359.3597398(34-37)Online publication date: 26-Jun-2023
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