EdgeRank

Formula and factors

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

∑ e d g e s e 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.

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).

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.