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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 ...
A WebDAV request may contain many sub-requests involving file operations, requiring a long time to complete the request. This code indicates that the server has received and is processing the request, but no response is available yet. [3] This prevents the client from timing out and assuming the request was lost. The status code is deprecated. [4]
Algospeak is the use of coded expressions to evade automated moderation algorithms on social media platforms such as TikTok and YouTube.It is used to discuss topics deemed sensitive to moderation algorithms while avoiding penalties such as shadow banning or downranking of content.
HTTP/3 uses similar semantics compared to earlier revisions of the protocol, including the same request methods, status codes, and message fields, but encodes them and maintains session state differently. However, partially due to the protocol's adoption of QUIC, HTTP/3 has lower latency and loads more quickly in real-world usage when compared ...
YouTube is an American social media and online video sharing platform owned by Google. YouTube was founded on February 14, 2005, by Steve Chen, Chad Hurley, and Jawed Karim, three former employees of PayPal. Headquartered in San Bruno, California, it is the second-most-visited website in the world, after Google Search.
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 ...
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]
It provides a gradient boosting framework which, among other features, attempts to solve for categorical features using a permutation-driven alternative to the classical algorithm. [7] It works on Linux , Windows , macOS , and is available in Python , [ 8 ] R , [ 9 ] and models built using CatBoost can be used for predictions in C++ , Java ...