# Algorithmic Extremism: Examining YouTube's Rabbit Hole of Radicalization

arXiv: https://arxiv.org/abs/1912.11211 · HTML: https://mlsanity.com/paper/1912.11211/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Dec 2019.

## Built on by (most cited first, 13 of 13)

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- [Harmful YouTube Video Detection: A Taxonomy of Online Harm and MLLMs as Alternative Annotators](https://mlsanity.com/paper/2411.05854/) (2024)
- [Rewiring What-to-Watch-Next Recommendations to Reduce Radicalization Pathways](https://mlsanity.com/paper/2202.00640/) (2022)
- [A Novel BERT-based Classifier to Detect Political Leaning of YouTube Videos based on their Titles](https://mlsanity.com/paper/2404.04261/) (2024)
- [System-2 Recommenders: Disentangling Utility and Engagement in Recommendation Systems via Temporal Point-Processes](https://mlsanity.com/paper/2406.01611/) (2024)
- [Quantifying the Potential to Escape Filter Bubbles: A Behavior-Aware Measure via Contrastive Simulation](https://mlsanity.com/paper/2512.03067/) (2025)
- [Reducing Exposure to Harmful Content via Graph Rewiring](https://mlsanity.com/paper/2306.07930/) (2023)
- [The Transformation Risk-Benefit Model of Artificial Intelligence: Balancing Risks and Benefits Through Practical Solutions and Use Cases](https://mlsanity.com/paper/2406.11863/) (2024)
- [MetaHarm: Harmful YouTube Video Dataset Annotated by Domain Experts, GPT-4-Turbo, and Crowdworkers](https://mlsanity.com/paper/2504.16304/) (2025)
- [Examining the Role of YouTube Production and Consumption Dynamics on the Formation of Extreme Ideologies](https://mlsanity.com/paper/2603.08049/) (2026)

## Abstract (arXiv)

The role that YouTube and its behind-the-scenes recommendation algorithm plays in encouraging online radicalization has been suggested by both journalists and academics alike. This study directly quantifies these claims by examining the role that YouTube's algorithm plays in suggesting radicalized content. After categorizing nearly 800 political channels, we were able to differentiate between political schemas in order to analyze the algorithm traffic flows out and between each group. After conducting a detailed analysis of recommendations received by each channel type, we refute the popular radicalization claims. To the contrary, these data suggest that YouTube's recommendation algorithm actively discourages viewers from visiting radicalizing or extremist content. Instead, the algorithm is shown to favor mainstream media and cable news content over independent YouTube channels with slant towards left-leaning or politically neutral channels. Our study thus suggests that YouTube's recommendation algorithm fails to promote inflammatory or radicalized content, as previously claimed by several outlets.