The Facebook EdgeRank Algorithm

What is EdgeRank?

EdgeRank is a Facebook algorithm that determines what updates and stories appear in a user's news feed. The news feed is the main screen a user is shown when logged in; the screen where you see all of your friends' updates. With millions of users, companies and brands on Facebook, there is a continuous creation of masses of content. In fact, every action (except clicks) a user takes on Facebook is eligible to become a story on the news feed. EdgeRank affects the content that you post especially content for your Facebook Page where you need to reach your audience as much as possible. What EdgeRank does is that it filters out stories from the users' news feed so only interesting stories appear for every different user on Facebook. This way every user's news feed is not cluttered with boring content; instead it is filled with interesting content that makes the user spend more time on it. Your post must meet EdgeRank main variables (requirements) so your content will appear in other users' news feeds. The three major variables are called affinity, weight and time decay. These will be described in detail below:

Affinity

The affinity variable takes into consideration how connected the user is to the user that created the story. If Joseph interacts or posts frequently on Alan's wall for instance, then Joseph is more likely to see a post from Alan. All the actions taken on Facebook such as clicking, liking, commenting, tagging and so on will affect the affinity for a particular story. It is also worth mentioning that affinity is one way. Hence, Joseph may have a different affinity score to Alan, than Alan has to Joseph.

Weight

A particular action on Facebook will carry more weight. A simple 'Like', for example, will have less weight than a comment, since a comment requires more effort. Joseph then is more likely to see a post of Alan commenting on a post than just a 'Like' on the post. However, a post with more interactions can be assigned more weight than a post with no interactions.

Time Decay

To keep the news feed fresh, as the story gets older it 'decays' and becomes irrelevant. Without the time decay variable the news feed would be inundated with popular content but that is a couple of days old. Hence, the time decay variable brings up fresh content to the news feed more frequently. Time decay also takes to consideration the amount and the last time the user logged in to Facebook. For instance, a user that did not log in a week may see popular posts that are a few days old, not that fresh but still relevant for that user.

EdgeRank matters since a lot of users spend their time on the news feed then on the friends' profile or fan pages. 96% of fans do not go back to the brand's Facebook page from when they 'liked' the page. Thus knowing the basics of the EdgeRank algorithm will increase the effectiveness of your marketing posts.

Conrad Bugeja is a Search Engine Optimisation Consultant and Pay-Per-Click Consultant at Alert eBusiness Internet Marketing Division - www.alertemarketing.com