EdgeRank is a formula developed by Facebook[1] to determine what content appears in a user’s News Feed. It was simplified in 2010 and primarily utilizes three components: user affinity, content weight, and a time-based decay parameter. The precise methods to tweak these factors are kept secret by Facebook. Interestingly, different user reactions can influence organic reach, with ‘like’ reactions potentially reducing it, while ‘haha’, ‘love’ reactions, and comments can boost it. This algorithm[2] can significantly shape users’ online experiences, sometimes leading to filter bubbles or even mood alterations. The average engagement rate for Facebook pages is less than 1%, while most non-profit pages achieve an organic reach of 10% or less. This algorithm is a crucial aspect of Facebook’s News Feed, impacting user interaction and engagement rates.
EdgeRank is the name commonly given to the algorithm that Facebook uses to determine what articles should be displayed in a user's News Feed. As of 2011, Facebook has stopped using the EdgeRank system and uses a machine learning algorithm that, as of 2013, takes more than 100,000 factors into account.
EdgeRank was developed and implemented by Serkan Piantino.