# 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.[1]

EdgeRank was developed and implemented by Serkan Piantino.

## Formula and factors

In 2010, a simplified version of the EdgeRank algorithm was presented as:

${\displaystyle \sum _{\mathrm {edges\,} e}u_{e}w_{e}d_{e}}$

where:

${\displaystyle u_{e}}$ is user affinity.
${\displaystyle w_{e}}$ is how the content is weighted.
${\displaystyle d_{e}}$ 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).[1]
• Content Weight: What action was taken by the user on the content.[1]
• Time-Based Decay Parameter: New or old. Newer posts tend to hold a higher place than older posts.[1]

Some of the methods that Facebook uses to adjust the parameters are proprietary and not available to the public.[2]

## 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 (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).[3]

As a result, for Facebook pages, the typical engagement rate is less than 1 % (or less than 0.1 % for the bigger ones)[4] and organic reach 10 % or less for most non-profits.[5]

As a consequence, for pages it may be nearly impossible to reach any significant audience without paying to promote their content.[6]