"Just as algorithms could be trained to predict who would click what ad, they could also be trained to predict who would like or share what post, and then give those posts more prominence"
#longread on the history of 'engagement' maximization at Facebook: technologyreview.com/2021/03/11/102…
#longread on the history of 'engagement' maximization at Facebook: technologyreview.com/2021/03/11/102…
"Teams had previously used design tactics, like experimenting with the content and frequency of notifications, to try to hook users more effectively. Their goal…was to increase a metric called L6/7, the fraction of people who logged in to Facebook six of the previous seven days"
After introducing ml-based optimization, "every user interaction once analyzed by engineers was being analyzed by algorithms. Those algorithms were creating ...more personalized feedback loops for tweaking and tailoring each user’s news feed to keep nudging up engagement numbers"
"The process is still the same today. Teams train up a new machine-learning model on FBLearner ... Then they test the new model on a small subset of Facebook’s users to measure how it changes engagement metrics, such as the number of likes, comments, and shares"
"If a model reduces engagement too much, it’s discarded"
• • •
Missing some Tweet in this thread? You can try to
force a refresh
