Publication detail

Auditing YouTube's Recommendation Algorithm for Misinformation Filter Bubbles

SRBA, I. MÓRO, R. TOMLEIN, M. PECHER, B. ŠIMKO, J. ŠTEFANCOVÁ, E. KOMPAN, M. HRČKOVÁ, A. PODROUŽEK, J. GAVORNÍK, A. BIELIKOVÁ, M.

Original Title

Auditing YouTube's Recommendation Algorithm for Misinformation Filter Bubbles

Type

journal article - other

Language

English

Original 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.

Keywords

Audit, recommender systems, filter bubble, misinformation, personalization, automatic labeling, ethics, YouTube

Authors

SRBA, I.; MÓRO, R.; TOMLEIN, M.; PECHER, B.; ŠIMKO, J.; ŠTEFANCOVÁ, E.; KOMPAN, M.; HRČKOVÁ, A.; PODROUŽEK, J.; GAVORNÍK, A.; BIELIKOVÁ, M.

Released

27. 1. 2023

ISBN

2770-6699

Periodical

ACM transactions on recommender systems

Year of study

1

Number

1

State

United States of America

Pages from

1

Pages to

33

Pages count

33

URL

https://dl.acm.org/doi/10.1145/3568392

BibTex

@article{BUT180410,
  author="SRBA, I. and MÓRO, R. and TOMLEIN, M. and PECHER, B. and ŠIMKO, J. and ŠTEFANCOVÁ, E. and KOMPAN, M. and HRČKOVÁ, A. and PODROUŽEK, J. and GAVORNÍK, A. and BIELIKOVÁ, M.",
  title="Auditing YouTube's Recommendation Algorithm for Misinformation Filter Bubbles",
  journal="ACM transactions on recommender systems",
  year="2023",
  volume="1",
  number="1",
  pages="1--33",
  doi="10.1145/3568392",
  issn="2770-6699",
  url="https://dl.acm.org/doi/10.1145/3568392"
}

Documents

_ACM_TORS_2022__TBD.pdf