Publicly, the platform says it’s trying to do what it can to minimize the amplification of extreme content. But it’s still looking for ways to keep users on the site.
....MUCH MORERecommendation algorithms are some of the most powerful machine-learning systems today because of their ability to shape the information we consume. YouTube’s algorithm, especially, has an outsize influence. The platform is estimated to be second only to Google in web traffic, and 70% of what users watch is fed to them through recommendations.In recent years, this influence has come under heavy scrutiny. Because the algorithm is optimized for getting people to engage with videos, it tends to offer choices that reinforce what someone already likes or believes, which can create an addictive experience that shuts out other views. This also often rewards the most extreme and controversial videos, which studies have shown can quickly push people into deep rabbit holes of content and lead to political radicalization.While YouTube has publicly said that it’s working on addressing these problems, a new paper from Google, which owns YouTube, seems to tell a different story. It proposes an update to the platform’s algorithm that is meant to recommend even more targeted content to users in the interest of increasing engagement.Here’s how YouTube’s recommendation system currently works. To populate the recommended-videos sidebar, it first compiles a shortlist of several hundred videos by finding ones that match the topic and other features of the one you are watching. Then it ranks the list according to the user’s preferences, which it learns by feeding all your clicks, likes, and other interactions into a machine-learning algorithm....
