mlsanityData to 14 Oct 2026

Auditing Radicalization Pathways on YouTube

arXiv 1908.08313 ↗First posted 22 Aug 2019cs.CY cs.SIcited by 53

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Abstract (arXiv)

Non-profits, as well as the media, have hypothesized the existence of a radicalization pipeline on YouTube, claiming that users systematically progress towards more extreme content on the platform. Yet, there is to date no substantial quantitative evidence of this alleged pipeline. To close this gap, we conduct a large-scale audit of user radicalization on YouTube. We analyze 330,925 videos posted on 349 channels, which we broadly classified into four types: Media, the Alt-lite, the Intellectual Dark Web (I.D.W.), and the Alt-right. According to the aforementioned radicalization hypothesis, channels in the I.D.W. and the Alt-lite serve as gateways to fringe far-right ideology, here represented by Alt-right channels. Processing 72M+ comments, we show that the three channel types indeed increasingly share the same user base; that users consistently migrate from milder to more extreme content; and that a large percentage of users who consume Alt-right content now consumed Alt-lite and I.D.W. content in the past. We also probe YouTube's recommendation algorithm, looking at more than 2M video and channel recommendations between May/July 2019. We find that Alt-lite content is easily reachable from I.D.W. channels, while Alt-right videos are reachable only through channel recommendations. Overall, we paint a comprehensive picture of user radicalization on YouTube.

Key facts. Cited by 53 papers in the map (data through 14 Oct 2026). First posted on arXiv 22 Aug 2019.

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Citing papers per year: 2021: 9; 2022: 12; 2023: 5; 2024: 11; 2025: 4; 2026: 12.

53 papers.

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  14. 2024Harm Mitigation in Recommender Systems under User Preference Dynamicscited by 5
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  19. 2021Modeling Language Usage and Listener Engagement in Podcastscited by 2
  20. 2021Predicting the Factuality of Reporting of News Media Using Observations About User Attention in Their YouTube Channelscited by 2
  21. 2024A Novel BERT-based Classifier to Detect Political Leaning of YouTube Videos based on their Titlescited by 2
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  26. 2024Algorithmic Behaviors Across Regions: A Geolocation Audit of YouTube Search for COVID-19 Misinformation Between the United States and South Africacited by 1
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  32. 2025MetaHarm: Harmful YouTube Video Dataset Annotated by Domain Experts, GPT-4-Turbo, and Crowdworkerscited by 0
  33. 2026Examining the Role of YouTube Production and Consumption Dynamics on the Formation of Extreme Ideologiescited by 0
  34. 2026DariMis: Harm-Aware Modeling for Dari Misinformation Detection on YouTubecited by 0
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