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EdgeRank is the name commonly given to the
algorithm In mathematics and computer science, an algorithm () is a finite sequence of Rigour#Mathematics, mathematically rigorous instructions, typically used to solve a class of specific Computational problem, problems or to perform a computation. Algo ...
that
Facebook Facebook is a social media and social networking service owned by the American technology conglomerate Meta Platforms, Meta. Created in 2004 by Mark Zuckerberg with four other Harvard College students and roommates, Eduardo Saverin, Andre ...
uses to determine what articles should be displayed in a user's
News Feed Facebook's Feed, formerly known as the News Feed, is a web feed feature for the social network. The feed is the primary system through which users are exposed to content posted on the network. Feed highlights information that includes profile ...
. As of 2011, Facebook has stopped using the EdgeRank system and uses a
machine learning Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of Computational statistics, statistical algorithms that can learn from data and generalise to unseen data, and thus perform Task ( ...
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: :\sum_ u_e w_e d_e where: : u_e is user affinity. : w_e is how the content is weighted. : d_e is a time-based decay parameter. * 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 (post/status update). * 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 (e.g., the "haha" reaction or "comments") 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 A filter bubble or ideological frame is a state of intellectual isolationTechnopediaDefinition – What does Filter Bubble mean?, Retrieved October 10, 2017, "....A filter bubble is the intellectual isolation, that can occur when websites make ...
(if users are exposed to updates which confirm their opinions etc.) or alter people's mood (if users are shown a disproportionate amount of positive or negative updates). As a result, for Facebook pages, the typical engagement rate is less than 1% (or less than 0.1% for the bigger ones), 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.


See also

*
PageRank PageRank (PR) is an algorithm used by Google Search to rank web pages in their search engine results. It is named after both the term "web page" and co-founder Larry Page. PageRank is a way of measuring the importance of website pages. Accordin ...
, the ranking algorithm used by Google's search engine


References


External links


edgerank.net

Facebook - How News Feed Works
Facebook Algorithms {{Web-stub