EdgeRank


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.

Formula and factors

In 2010, a simplified version of the EdgeRank algorithm was presented as:
where:
  • User Affinity: The User Affinity part of the algorithm in Facebook's EdgeRank looks at the relationship and proximity of the user and the content.
  • Content Weight: What action was taken by the user on the content.
  • Time-Based Decay Parameter: New or old. Newer posts tend to hold a higher place than older posts.
Some of the methods that Facebook uses to adjust the parameters are proprietary and not available to the public.
A study has shown that it is possible to hypothesize a disadvantage of the "like" reaction and advantages of other interactions in content algorithmic ranking on Facebook. The "like" button can decrease the organic reach as a "brake effect of viral reach".  The "haha" reaction, "comments" and the "love" reaction could achieve the highest increase in total organic reach.

Impact

EdgeRank and its successors have a broad impact on what users actually see out of what they ostensibly follow: for instance, the selection can produce a filter bubble or alter people's mood.
As a result, for Facebook pages, the typical engagement rate is less than 1%, and organic reach 10% or less for most non-profits.
As a consequence, for pages, it may be nearly impossible to reach any significant audience without paying to promote their content.