mlsanityData to 14 Oct 2026

Algorithmic Behaviors Across Regions: A Geolocation Audit of YouTube Search for COVID-19 Misinformation Between the United States and South Africa

arXiv 2409.10168 ↗First posted 16 Sep 2024cs.CY cs.AI cs.HCcited by 1

Not on the map. Why?

Abstract (arXiv)

Despite being an integral tool for finding health-related information online, YouTube has faced criticism for disseminating COVID-19 misinformation globally to its users. Yet, prior audit studies have predominantly investigated YouTube within the Global North contexts, often overlooking the Global South. To address this gap, we conducted a comprehensive 10-day geolocation-based audit on YouTube to compare the prevalence of COVID-19 misinformation in search results between the United States (US) and South Africa (SA), the countries heavily affected by the pandemic in the Global North and the Global South, respectively. For each country, we selected 3 geolocations and placed sock-puppets, or bots emulating "real" users, that collected search results for 48 search queries sorted by 4 search filters for 10 days, yielding a dataset of 915K results. We found that 31.55% of the top-10 search results contained COVID-19 misinformation. Among the top-10 search results, bots in SA faced significantly more misinformative search results than their US counterparts. Overall, our study highlights the contrasting algorithmic behaviors of YouTube search between two countries, underscoring the need for the platform to regulate algorithmic behavior consistently across different regions of the Globe.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 22 of its 92 references that are in the map. First posted on arXiv 16 Sep 2024.

Builds on

22 of 92 references.

References · 22

  1. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,166
  2. 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,375
  3. 2016Enriching Word Vectors with Subword Informationcited by 1,583
  4. 2021DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharingcited by 1,254
  5. 2020MPNet: Masked and Permuted Pre-training for Language Understandingcited by 981
  6. 2022Fine-tuned Language Models are Continual Learnerscited by 156
  7. 2019Auditing Radicalization Pathways on YouTubecited by 53
  8. 2023Who Audits the Auditors? Recommendations from a field scan of the algorithmic auditing ecosystemcited by 52
  9. 2024"They are uncultured": Unveiling Covert Harms and Social Threats in LLM Generated Conversationscited by 25
  10. 2019Towards Automatic Detection of Misinformation in Online Medical Videoscited by 22
  11. 2022An Audit of Misinformation Filter Bubbles on YouTube: Bubble Bursting and Recent Behavior Changescited by 15
  12. 2020Rapidly Bootstrapping a Question Answering Dataset for COVID-19cited by 14
10 more
  1. 2023YouNICon: YouTube's CommuNIty of Conspiracy Videoscited by 13
  2. 2024ValueScope: Unveiling Implicit Norms and Values via Return Potential Model of Social Interactionscited by 12
  3. 2021Auditing E-Commerce Platforms for Algorithmically Curated Vaccine Misinformationcited by 11
  4. 2020"It is just a flu": Assessing the Effect of Watch History on YouTube's Pseudoscientific Video Recommendationscited by 10
  5. 2020A Longitudinal Analysis of YouTube's Promotion of Conspiracy Videoscited by 9
  6. 2022An Empirical Investigation of Personalization Factors on TikTokcited by 9
  7. 2017Evaluation Measures for Relevance and Credibility in Ranked Listscited by 6
  8. 2022Deradicalizing YouTube: Characterization, Detection, and Personalization of Religiously Intolerant Arabic Videoscited by 4
  9. 2023Assessing enactment of content regulation policies: A post hoc crowd-sourced audit of election misinformation on YouTubecited by 2
  10. 2022Subscriptions and external links help drive resentful users to alternative and extremist YouTube videoscited by 1

Built on by

Cited by year

Citing papers per year: 2025: 1.

1 paper.