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YouTube's algorithm is accountable for roughly 70% of users' recommended videos and what drives people to watch certain content. [20] According to a 2022 study by the Mozilla Foundation, users have little power to keep unsolicited videos out of their suggested recommended content. This includes videos about hate speech, livestreams, etc. [21] [20]
YouTube has a pattern of recommending right-leaning and Christian videos, even to users who haven’t previously interacted with that kind of content, according to a recent study of the platform ...
YouTube has faced criticism over aspects of its operations, [217] its recommendation algorithms perpetuating videos that promote conspiracy theories and falsehoods, [218] hosting videos ostensibly targeting children but containing violent or sexually suggestive content involving popular characters, [219] videos of minors attracting pedophilic ...
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]
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.
Though viewership of far-right videos peaked in 2017—before YouTube's 2019 algorithm changes—through at least 2020 YouTube remained the only major social networking platform that was more popular among right-leaning users. [97] In 2019–2020, mainstream conservatives fueled most growth in both video production and viewership. [97]
Recently leaked documents suggest that the social media platform TikTok had policies that limited the exposure of certain users based on looks.
Because side-channel attacks rely on the relationship between information emitted (leaked) through a side channel and the secret data, countermeasures fall into two main categories: (1) eliminate or reduce the release of such information and (2) eliminate the relationship between the leaked information and the secret data, that is, make the leaked information unrelated, or rather uncorrelated ...