Definition:
EdgeRank is the name historically used for a Facebook system that ranked feed posts according to their relevance to each user. This algorithm helped select which content to display from the available updates, rather than showing it solely in publication order.
Its explanation was organised around three factors:
- Affinity: the relationship between the user viewing the content and the person or page publishing it, estimated from their interactions. It was not limited to a page’s follower count.
- Weight: the value assigned to different types of content or interaction. It did not simply mean adding up the likes and shares a post received.
- Time decay: the loss of weight associated with elapsed time. It favoured recent content but worked alongside the other factors, rather than producing a purely chronological order.
EdgeRank is a historical reference. Its three factors do not, on their own, describe the systems Facebook currently uses or allow a public visibility score to be calculated for a page.
Table of contents
Evolution of Facebook’s algorithm
Feed ranking evolved towards systems that combine numerous signals and predictions. The relationship with the publisher, previous interactions and recency remain relevant, but they do not form a fixed formula sufficient to explain content selection.
Facebook uses artificial intelligence models to estimate which posts may interest each person. Selection includes content from followed accounts and recommendations from other accounts, and may vary between surfaces such as the feed and Reels.
Meta explains that its systems combine different predictions about the value of content, alongside behavioural signals and feedback collected from users. Predicting that someone will share a post is only one part of that assessment.
Users’ expressed preferences and measures that reduce the distribution of certain problematic or low-quality content also play a role. Counting interactions alone therefore cannot explain why one post appears before another.
Strategies to improve visibility on Facebook
The following actions help develop content and its relationship with the audience, but they do not directly modify ranking systems or guarantee an increase in reach:
- Encouraging relevant conversations: questions, polls and multimedia posts can facilitate participation when they make sense for the audience. Asking for likes, comments or shares solely to manipulate distribution may be considered engagement bait and reduce visibility.
- Maintaining a sustainable frequency: regular posting provides continuity in communication. Publishing more does not automatically increase affinity or guarantee that followers see every post.
- Analysing publication times: comparing times and results can help identify when the audience responds. Timing is one variable among others, and there is no universal best time for every page.
Impact of the algorithm on digital marketing
Content selection influences how posts reach users. To interpret its effects, it is useful to distinguish the following aspects:
- Organic reach: organic reach depends on unpaid distribution. Understanding the systems can guide analysis, but it does not guarantee greater reach or remove the need for advertising on its own.
- Audience understanding: studying which content generates interest helps adapt topics and messages. This is not the same as controlling who receives each post or configuring targeting for an advertising campaign.
- Measuring results: reach, impressions, interactions, clicks and conversions describe different outcomes. Comparing equivalent posts and periods helps assess changes without attributing every variation to the algorithm.
EdgeRank is useful for understanding a predecessor of personalised ranking. Current decisions should be based on the platform’s documented operation and observed results, not on applying its three historical factors literally.