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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 ...
The study noted that YouTube’s recommendation algorithm “drives 70% of all video views.” ... The researchers also found that YouTube recommended videos including sexually explicit content to ...
In 2019, YouTube announced a change to its recommendation algorithm to reduce conspiracy theory related content. [12] [18] Some extreme content, such as explicit depictions of violence, are typically removed on most social media platforms. On YouTube, content that expresses support of extremism may have monetization features removed, may be ...
YouTube's content recommendation algorithm is designed to keep the user engaged as long as possible, which Roose calls the "rabbit hole effect". [5] The podcast features interviews with a variety of people involved with YouTube and the "rabbit hole effect". [6] For instance, in episode four Roose interviews Susan Wojcicki—the CEO of YouTube. [2]
A recommender system (RecSys), or a recommendation system (sometimes replacing system with terms such as platform, engine, or algorithm), is a subclass of information filtering system that provides suggestions for items that are most pertinent to a particular user.
YouTube’s algorithm is recommending videos about disordered eating and weight loss to some young teens, a new study says. ... The researchers then took note of the top 10 recommendations in the ...
However, its recommendation algorithm has been shown to recommend extremist content, especially far-right and conspiracy propaganda, leading to claims that YouTube has been used as a tool for political radicalization. Concurrently, the website has been criticized for inadequately policing against false or misleading content.
The user based top-N recommendation algorithm uses a similarity-based vector model to identify the k most similar users to an active user. After the k most similar users are found, their corresponding user-item matrices are aggregated to identify the set of items to be recommended.