Facebook Edgerank Algorithm Explained

Facebook’s Edgerank is probably now the second most used search algorithm. Google’s (natural) search engine  algorithm is the first… Bing & Yahoo used to have a look in, and until recently it’s been Apple and Amazon but thanks to Facebook’s ‘always-on‘ nature the Edgerank algorithm is hard at work in the background.

Technically it’s not a search algorithm – it’s more like a measurement tool for reach and impact. But understanding what it is and how it works no doubt helps you understand how to lay out your stall better to ensure your marketing is maximised within the Facebook marketplace.

Simply put the algorithm determines what appears in Facebooks users’ news feeds. Here’s the basic principle:

  1. Facebook looks at whether or not you’ve previously interacted with an author’s posts or whether or not your friends are engaging around those posts
  2. If content is or isn’t engaged by your friends and the network at large, affects what you see and what you don’t see
  3. EdgeRank also examines whether or not your have interacted with similar types of posts in the past, i.e. photos, videos, polls, etc.
  4. If content or page hosts have received complaints by other users, chances are that you will not see it.

This is all stacked up against time – engagement very quickly drops off after an update post. As Mark Lock on Business Insider noted in 2011 the half life of the average Facebook update post is 1hr 20mins!

facebook-like-engagement-rate

The algorithm itself looks like the below, but here is a brief definition from Techcrunch:

…every item that shows up in your News Feed is considered an Object. If you have an Object in the News Feed (say, a status update), whenever another user interacts with that Object they’re creating what Facebook calls an Edge, which includes actions like tags and comments.

edgerank

Affinity

Affinity is a score based on the proximity or how “friendly” you are with someone. You’ve probably seen this in action. Comment on someone’s photos and you’ll find them appearing in your feed more often. As Kelvin Newman points out in his definitive Econsultancy article “…affinity is one-way. This means you visiting a forgotten friends profile doesn’t increase the likelihood of you appearing in their newsfeed.” So you can’t dupe the algorithm this way!

Edge Weight

This is a formula which decides which pieces of content are more likely to appear in news feeds than others. So photos are perceived as more important than someone “liking” a business profile for example.

The three obvious content types for this classification are  video, photos, and links. Incorporating objects with high weight scores will help you reach as many followers as possible.

Individual’s Edge Weight is also different. Someone who likes browsing photos is more likely to have them in their feed than someone who doesn’t.

Decay

Decay, or recency, adds the dimension of old news. Unlike Twitter’s chronological order Facebook simply likes as much new content as possible by preferencing it.

There’s little one can do to maximise this apart from – you got it – creating more new compelling useful content. You can also play with when to release this content. Facebook’s analytics platform is great for this. Sometimes it’s better to publish when the competition aren’t but it’s still important to publish when people need that content.

Lots of food for though for brands and individuals alike. As Brian Solis says over on his blog “Social Media Optimization is the New SEO”!

As for the future… here is what Forbes had to say last September:

And now Facebook is getting into search. At a Disrupt conference last week, Mark Zuckerberg explained that search engines are evolving into places where users go for answers, and that Facebook is uniquely positioned to compete in that market: “And when you think about it from that perspective, Facebook is pretty uniquely positioned to answer a lot of the questions that people have. So what sushi restaurants have my friends gone to in New York in the past six months and liked? . . . . These are queries that you could potentially do at Facebook if we build out this system that you just couldn’t do anywhere else.”

More on the new Facebook Graph search to some on the ThinkSearch blog.

I don’t know about you but I still feel like edgerank is in it’s infancy. It’s buggy, sometimes duplicates posts, and could definitely benefit business more by being explained more. As an algorithm it doesn’t need to be a closely guarded one as it’s about maximising engagement, and learning how to improve this is good for everyone.

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