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

arXiv: https://arxiv.org/abs/2409.10168 · HTML: https://mlsanity.com/paper/2409.10168/

## Key facts

- Cited by 1 papers 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 (most cited first, 22 of 22)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing](https://mlsanity.com/paper/2111.09543/) (2021)
- [MPNet: Masked and Permuted Pre-training for Language Understanding](https://mlsanity.com/paper/2004.09297/) (2020)
- [Fine-tuned Language Models are Continual Learners](https://mlsanity.com/paper/2205.12393/) (2022)
- [Auditing Radicalization Pathways on YouTube](https://mlsanity.com/paper/1908.08313/) (2019)
- [Who Audits the Auditors? Recommendations from a field scan of the algorithmic auditing ecosystem](https://mlsanity.com/paper/2310.02521/) (2023)
- ["They are uncultured": Unveiling Covert Harms and Social Threats in LLM Generated Conversations](https://mlsanity.com/paper/2405.05378/) (2024)
- [Towards Automatic Detection of Misinformation in Online Medical Videos](https://mlsanity.com/paper/1909.01543/) (2019)
- [An Audit of Misinformation Filter Bubbles on YouTube: Bubble Bursting and Recent Behavior Changes](https://mlsanity.com/paper/2203.13769/) (2022)
- [Rapidly Bootstrapping a Question Answering Dataset for COVID-19](https://mlsanity.com/paper/2004.11339/) (2020)
- [YouNICon: YouTube's CommuNIty of Conspiracy Videos](https://mlsanity.com/paper/2304.05274/) (2023)
- [ValueScope: Unveiling Implicit Norms and Values via Return Potential Model of Social Interactions](https://mlsanity.com/paper/2407.02472/) (2024)
- [Auditing E-Commerce Platforms for Algorithmically Curated Vaccine Misinformation](https://mlsanity.com/paper/2101.08419/) (2021)
- ["It is just a flu": Assessing the Effect of Watch History on YouTube's Pseudoscientific Video Recommendations](https://mlsanity.com/paper/2010.11638/) (2020)
- [A Longitudinal Analysis of YouTube's Promotion of Conspiracy Videos](https://mlsanity.com/paper/2003.03318/) (2020)
- [An Empirical Investigation of Personalization Factors on TikTok](https://mlsanity.com/paper/2201.12271/) (2022)
- [Evaluation Measures for Relevance and Credibility in Ranked Lists](https://mlsanity.com/paper/1708.07157/) (2017)
- [Deradicalizing YouTube: Characterization, Detection, and Personalization of Religiously Intolerant Arabic Videos](https://mlsanity.com/paper/2207.00111/) (2022)
- [Assessing enactment of content regulation policies: A post hoc crowd-sourced audit of election misinformation on YouTube](https://mlsanity.com/paper/2302.07836/) (2023)
- [Subscriptions and external links help drive resentful users to alternative and extremist YouTube videos](https://mlsanity.com/paper/2204.10921/) (2022)

## Built on by (most cited first, 1 of 1)

- [Content and Engagement Trends in COVID-19 YouTube Videos: Evidence from the Late Pandemic](https://mlsanity.com/paper/2509.01954/) (2025)

## 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.