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  2. Algorithmic radicalization - Wikipedia

    en.wikipedia.org/wiki/Algorithmic_radicalization

    Algorithmic radicalization is the concept that recommender algorithms on popular social media sites such as YouTube and Facebook drive users toward progressively more extreme content over time, leading to them developing radicalized extremist political views. Algorithms record user interactions, from likes/dislikes to amount of time spent on ...

  3. BrownBoost - Wikipedia

    en.wikipedia.org/wiki/BrownBoost

    BrownBoost is a boosting algorithm that may be robust to noisy datasets. BrownBoost is an adaptive version of the boost by majority algorithm. As is the case for all boosting algorithms, BrownBoost is used in conjunction with other machine learning methods. BrownBoost was introduced by Yoav Freund in 2001. [1]

  4. Alt-right pipeline - Wikipedia

    en.wikipedia.org/wiki/Alt-right_pipeline

    The alt-right pipeline (also called the alt-right rabbit hole) is a proposed conceptual model regarding internet radicalization toward the alt-right movement. It describes a phenomenon in which consuming provocative right-wing political content, such as antifeminist or anti-SJW ideas, gradually increases exposure to the alt-right or similar far-right politics.

  5. YouTube's algorithm more likely to recommend users ... - AOL

    www.aol.com/news/youtube-algorithm-more-likely...

    The study noted that YouTube’s recommendation algorithm “drives 70% of all video views.” ... This isn’t the first time YouTube has faced scrutiny for its algorithm.

  6. YouTube's algorithm pushes right-wing, explicit videos ... - AOL

    www.aol.com/news/youtubes-algorithm-pushes-wing...

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  7. YouTube automation - Wikipedia

    en.wikipedia.org/wiki/Youtube_Automation

    YouTube Automation is noted for its scalability, enabling channel owners to potentially expand their channel's reach and content output without a corresponding increase in manual labor or time commitment. The model's flexibility is further underscored by its operability from any location with internet access.

  8. XGBoost - Wikipedia

    en.wikipedia.org/wiki/XGBoost

    It runs on a single machine, as well as the distributed processing frameworks Apache Hadoop, Apache Spark, Apache Flink, and Dask. [ 9 ] [ 10 ] XGBoost gained much popularity and attention in the mid-2010s as the algorithm of choice for many winning teams of machine learning competitions .

  9. LightGBM - Wikipedia

    en.wikipedia.org/wiki/LightGBM

    LightGBM, short for Light Gradient-Boosting Machine, is a free and open-source distributed gradient-boosting framework for machine learning, originally developed by Microsoft. [4] [5] It is based on decision tree algorithms and used for ranking, classification and other machine learning tasks. The development focus is on performance and ...