What is EdgeRank?

EdgeRank was Facebook’s original algorithm designed to determine the placement and visibility of posts in a user’s News Feed. Introduced in 2009, it evaluated and ranked content based on three primary factors: user affinity, content weight, and time decay. This system aimed to ensure that users saw the most relevant and engaging content from their network.

How did EdgeRank work?

EdgeRank functioned by calculating a score for each piece of content (referred to as an “edge”) using the following components:

  • Affinity Score: This measured the relationship between the user and the content creator. Higher interaction frequency between a user and a friend or page resulted in a higher affinity score, making it more likely for that user’s content to appear in the News Feed.
  • Edge Weight: Different types of content carried different weights. For instance, photos and videos were considered more engaging and thus had higher weights compared to simple text updates. Additionally, the nature of user interactions influenced weight; comments were valued more than likes due to their higher level of engagement.
  • Time Decay: This factor considered the recency of the post. Newer posts were prioritized over older ones, ensuring that the News Feed remained timely and relevant.

The overall EdgeRank score was computed by multiplying these factors, and this score determined the prominence of a post in a user’s News Feed.

Evolution beyond EdgeRank

As Facebook’s platform grew and user interactions became more complex, the limitations of EdgeRank became apparent. By 2011, Facebook transitioned to a more sophisticated machine learning-based algorithm, moving away from the simplistic EdgeRank model. This new system evaluates thousands of signals to personalize News Feeds more effectively, considering factors such as:

  • The popularity of a post among all users.
  • The completeness of a user’s profile.
  • The type of device being used to access Facebook.

This shift allowed for a more nuanced and dynamic content ranking, adapting to the diverse behaviors and preferences of Facebook’s vast user base.

Implications for content creators

Understanding the principles behind EdgeRank and its evolution is crucial for content creators and marketers aiming to optimize their reach on Facebook. Emphasizing authentic engagement, producing high-quality content, and fostering meaningful interactions have always been key strategies. With the current algorithm’s complexity, it’s also important to stay updated with Facebook’s best practices and insights to ensure content remains visible and engaging to the intended audience.