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A Longitudinal Analysis of YouTube's Promotion of Conspiracy Videos

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arxiv 2003.03318 v1 pith:F3NRCTED submitted 2020-03-06 cs.CY cs.HCcs.IRcs.SI

classification cs.CYcs.HCcs.IRcs.SI
keywords conspiracyyoutubevideosclassifiertheoriesactivelyalgorithmalgorithms
verification ladder T0 review T1 audit T2 compute T3 formal
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Conspiracy theories have flourished on social media, raising concerns that such content is fueling the spread of disinformation, supporting extremist ideologies, and in some cases, leading to violence. Under increased scrutiny and pressure from legislators and the public, YouTube announced efforts to change their recommendation algorithms so that the most egregious conspiracy videos are demoted and demonetized. To verify this claim, we have developed a classifier for automatically determining if a video is conspiratorial (e.g., the moon landing was faked, the pyramids of Giza were built by aliens, end of the world prophecies, etc.). We coupled this classifier with an emulation of YouTube's watch-next algorithm on more than a thousand popular informational channels to obtain a year-long picture of the videos actively promoted by YouTube. We also obtained trends of the so-called filter-bubble effect for conspiracy theories.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Datasets for Navigating Sensitive Topics in Recommendation Systems

    cs.IR 2025-09 conditional novelty 5.0 of 10

    Two benchmark datasets link user-preference data (MovieLens, Archive of Our Own) with community content-warning labels to study sensitive-content exposure in recommender systems.

  2. Evaluating AI capabilities in detecting conspiracy theories on YouTube

    cs.CL 2025-05 conditional novelty 5.0 of 10

    Zero-shot text LLMs detect conspiracy YouTube videos with high recall but low precision, a fine-tuned RoBERTa remains competitive, and thumbnails add little value.

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