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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 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.
Central to the YouTube Automation business model are various streams of income, predominantly anchored by the YouTube Partner Program (YPP). In this program, channels generate revenue through advertisements displayed on their videos, with the income determined by the Cost Per Mille (CPM) metric that indicates the cost advertisers are willing to ...
YouTube's algorithm recommends right-wing, extremist videos to users — even if they haven't interacted with that content before. YouTube's algorithm pushes right-wing, explicit videos regardless ...
The report, titled "YouTube's Anorexia Algorithm," examines the first 1,000 videos that a teen girl would receive in the "Up Next" panel when watching videos about weight loss, diet or exercise ...
YouTube's algorithm more likely to recommend users right-wing and religious content, research finds. Victoria Feng. Updated June 18, 2024 at 8:13 AM. Didem Mente.
Generative AI can also be trained on the motions of a robotic system to generate new trajectories for motion planning or navigation. For example, UniPi from Google Research uses prompts like "pick up blue bowl" or "wipe plate with yellow sponge" to control movements of a robot arm. [ 78 ]
The algorithm achieved 96% accuracy on FaceForensics++, the only large-scale deepfake benchmark available at that time. The second generation [ 180 ] used end-to-end deep networks to differentiate between artifacts and high-level semantic facial information using two-branch networks.