research-article

Measuring Misinformation in Video Search Platforms: An Audit Study on YouTube

Authors:

Tanushree MitraAuthors Info & Claims

Proceedings of the ACM on Human-Computer Interaction, Volume 4, Issue CSCW1

Article No.: 48, Pages 1 - 27

https://doi.org/10.1145/3392854

Published: 29 May 2020 Publication History

Abstract

Search engines are the primary gateways of information. Yet, they do not take into account the credibility of search results. There is a growing concern that YouTube, the second largest search engine and the most popular video-sharing platform, has been promoting and recommending misinformative content for certain search topics. In this study, we audit YouTube to verify those claims. Our audit experiments investigate whether personalization (based on age, gender, geolocation, or watch history) contributes to amplifying misinformation. After shortlisting five popular topics known to contain misinformative content and compiling associated search queries representing them, we conduct two sets of audits-Search-and Watch-misinformative audits. Our audits resulted in a dataset of more than 56K videos compiled to link stance (whether promoting misinformation or not) with the personalization attribute audited. Our videos correspond to three major YouTube components: search results, Up-Next, and Top 5 recommendations. We find that demographics, such as, gender, age, and geolocation do not have a significant effect on amplifying misinformation in returned search results for users with brand new accounts. On the other hand, once a user develops a watch history, these attributes do affect the extent of misinformation recommended to them. Further analyses reveal a filter bubble effect, both in the Top 5 and Up-Next recommendations for all topics, except vaccine controversies; for these topics, watching videos that promote misinformation leads to more misinformative video recommendations. In conclusion, YouTube still has a long way to go to mitigate misinformation on its platform.

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Bonel ENannini LBassi DMaggini MSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)PositionProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3692243(4329-4344)Online publication date: 21-Jul-2024
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Index Terms

Measuring Misinformation in Video Search Platforms: An Audit Study on YouTube
1. Human-centered computing
  1. Collaborative and social computing
    1. Empirical studies in collaborative and social computing
  2. Human computer interaction (HCI)
    1. Empirical studies in HCI
2. Information systems
  1. World Wide Web
    1. Web searching and information discovery

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cover image Proceedings of the ACM on Human-Computer Interaction

Proceedings of the ACM on Human-Computer Interaction Volume 4, Issue CSCW1

CSCW

May 2020

1285 pages

EISSN:2573-0142

DOI:10.1145/3403424

Editors:
Cliff Lampe
University of Michigan
,
Jeff Nichols
Google
,
Karrie Karahalios
University of Illinois Urbana-Champaign
,
Geraldine Fitzpatrick
Vienna University of Technology
,
Uichin Lee
KAIST
,
Andres Monroy-Hernandez
Microsoft Research
,
Wolfgang Stuerzlinger
Simon Fraser University, Canada

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Published: 29 May 2020

Published in PACMHCI Volume 4, Issue CSCW1

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Bonel ENannini LBassi DMaggini MSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)PositionProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3692243(4329-4344)Online publication date: 21-Jul-2024
https://dl.acm.org/doi/10.5555/3692070.3692243
Chung MWihbey J(2024)The algorithmic knowledge gap within and between countries: Implications for combatting misinformationHarvard Kennedy School Misinformation Review10.37016/mr-2020-155Online publication date: 28-Aug-2024
https://doi.org/10.37016/mr-2020-155
Thakur NCui SKnieling VKhanna KShao M(2024)Investigation of the Misinformation about COVID-19 on YouTube Using Topic Modeling, Sentiment Analysis, and Language AnalysisComputation10.3390/computation1202002812:2(28)Online publication date: 6-Feb-2024
https://doi.org/10.3390/computation12020028
WALKER DRANANGA SISONG BMARIVATE V(2024)Generalising Across Domains in Video Misinformation Detection2024 IST-Africa Conference (IST-Africa)10.23919/IST-Africa63983.2024.10569650(1-8)Online publication date: 20-May-2024
https://doi.org/10.23919/IST-Africa63983.2024.10569650
Epstein RFlores A(2024)The Video Manipulation Effect (VME): A quantification of the possible impact that the ordering of YouTube videos might have on opinions and voting preferencesPLOS ONE10.1371/journal.pone.030303619:11(e0303036)Online publication date: 20-Nov-2024
https://doi.org/10.1371/journal.pone.0303036
Armstrong LLiu AMacNeil SMetaxa D(2024)The Silicon Ceiling: Auditing GPT’s Race and Gender Biases in HiringProceedings of the 4th ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization10.1145/3689904.3694699(1-18)Online publication date: 29-Oct-2024
https://dl.acm.org/doi/10.1145/3689904.3694699
Wang SHuang SZhou AMetaxa D(2024)Lower Quantity, Higher Quality: Auditing News Content and User Perceptions on Twitter/X Algorithmic versus Chronological TimelinesProceedings of the ACM on Human-Computer Interaction10.1145/36870468:CSCW2(1-25)Online publication date: 8-Nov-2024
https://dl.acm.org/doi/10.1145/3687046
Efstratiou AEfstratiou MYudhoatmojo SBlackburn JDe Cristofaro E(2024)"Here's Your Evidence": False Consensus in Public Twitter Discussions of COVID-19 ScienceProceedings of the ACM on Human-Computer Interaction10.1145/36870108:CSCW2(1-33)Online publication date: 8-Nov-2024
https://dl.acm.org/doi/10.1145/3687010
Lassiter TFleischmann K(2024)"Something Fast and Cheap" or "A Core Element of Building Trust"? - AI Auditing Professionals' Perspectives on Trust in AIProceedings of the ACM on Human-Computer Interaction10.1145/36869638:CSCW2(1-22)Online publication date: 8-Nov-2024
https://dl.acm.org/doi/10.1145/3686963
Pathiyan Cherumanal SGadiraju USpina D(2024)Everything We Hear: Towards Tackling Misinformation in PodcastsProceedings of the 26th International Conference on Multimodal Interaction10.1145/3678957.3678959(596-601)Online publication date: 4-Nov-2024
https://dl.acm.org/doi/10.1145/3678957.3678959
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