What is EdgeRank?

EdgeRank is the name commonly given to the algorithm that Facebook uses to determine what articles should be displayed in a user’s News Feed.

As of 2011, Facebook has stopped using the “EdgeRank” term internally to refer to its News Feed ranking algorithm, and in 2013, uses an algorithm that takes more than 100,000 factors into account in addition to EdgeRank’s three.

In 2010, the EdgeRank algorithm was described as:

Facebook Edgerank Algorithm
Edgerank = Affinity x Weight x Time Decay

Some of the methods that Facebook uses to adjust the parameters are proprietary and not available to the public.

How exactly does facebook Edgerank work?

You’re a recruiter and you’re trying to use Facebook for job postings. Let’s say your Facebook Page has 1,000 fans and you do an update about a really hot opportunity. The average Facebook user has 130 friends, meaning you have a potential reach of 130,000 users right? Wrong.

The average Facebook post only reaches 16% of the page’s fans and most fans never go back to the Page after they have liked it in the first place. The average user has no less than 130 friends and 80 Pages, groups and events to keep track of. This means some serious filtering has to be done to ensure a decent user experience.

Enter EdgeRank, Facebook’s algorithm for deciding what you will see in your newsfeed. This formula is based on 3 ingredients: Affinity, Weight and Time Decay.

Have a look at the infographic from VASimpleServices below:


EdgeRank Is Dead: Facebook’s News Feed Algorithm Now Has Close To 100K Weight Factors

The next time you tell a client how Facebook selects and ranks the content that shows up in the News Feed, you’ll need to do it without using the word EdgeRank.

EdgeRank, Facebook’s original News Feed ranking system, is dead.

Facebook hasn’t used the word internally for about two-and-a-half years. That’s when the company began employing a more complex ranking algorithm based on machine learning. The current News Feed algorithm doesn’t have a catchy name, but it’s clear from talking to the company’s engineers that EdgeRank is a thing of the past.

During a phone call this week, Lars Backstrom, Engineering Manager for News Feed Ranking at Facebook, estimated that there are as many as “100,000 individual weights in the model that produces News Feed.” The three original EdgeRank elements — Affinity, Weight and Time Decay — are still factors in News Feed ranking, but “other things are equally important,” he says.

In other words, the News Feed algorithm of today is much more sophisticated than just a couple years ago.

“The easiest analogy is to search engines and how they rank web pages,” Backstrom says. “It’s like comparing the Google of today with Alta Vista. Both Google and Bing have a lot of new signals, like personalization, that they use. It’s more sophisticated than the early days of search, when the words on a page were the most important thing.”

This has implications for marketers and business owners far beyond the wording used to describe News Feed rankings. It’s a reflection — and a cause, too — of today’s complex battle to reach Facebook users organically.

The winners? They’ll be the ones who understand how Facebook has moved past Affinity, Weight and Time Decay, and move past it themselves. Before we get into today’s News Feed algorithm, let’s go back a few years.

In The Beginning It Was … Turning Knobs
Facebook’s News Feed was born in September 2006, promising to provide … and I quote … “a personalized list of news stories throughout the day, so you’ll know when Mark adds Britney Spears to his Favorites or when your crush is single again.”

Yep, that’s a direct quote from the announcement. Cute, huh?

With the launch of News Feed, Facebook wanted to show users the most important content from their social network without making them click to visit their friends’ profiles. And it had to figure out a way to decide what was important to each person.

“In the beginning, News Feed ranking was turning knobs,” said Facebook VP of Product Chris Cox during Facebook’s recent News Feed media event. “Turn up photos a little bit, turn down platform stories a little bit.”

Cox gave a funny account of how he and a co-worker sat in Facebook’s offices and changed the ranking “knobs” based on feedback from users — feedback in the form of often angry emails and conversations with users outside the Facebook office.

Times were much simpler then.

From Knobs To EdgeRank
Facebook has obviously grown up a lot since then, particularly with the simultaneous launch of Facebook Ads and Pages in November 2007.

Businesses, clubs, and organizations began creating Facebook Pages and using them to try to reach existing and new fans. That meant more content and more chances for users’ News Feeds to get crowded and unwieldy.

The company advanced from “turning knobs” to EdgeRank, the algorithm that a) determined which of the thousands of stories (or “edges” as Facebook called them) qualified to show up in a user’s News Feed, and b) ranked them for display purposes. EdgeRank had three primary pieces:

Affinity — i.e., how close is the relationship between the user and the content/source?
Weight — i.e., what type of action was taken on the content?
Decay — i.e., how recent/current is the content?

EdgeRank made it possible for Facebook to give users a more personalized NewsFeed. As Cox explained, users that played a lot of games on Facebook could see more game-related content in their News Feed. Users that took part in a lot of Group discussions would see more content like that. And so forth.

From EdgeRank To… ?
With EdgeRank, the way you used Facebook largely determined what showed up in your News Feed. And it still does because, as Cox said last week, “We’re in the business of giving our users the most interesting possible experience every time they visit.”

But now that job is a lot more complicated than ever.

Consider that there are more than a billion people using Facebook each month. And 128 million in the U.S. that use Facebook every day. They’re using dozens of different mobile devices with different capabilities for displaying content. There are 18 million Pages, many of which are actively looking for attention and a way to show up the News Feed as often as possible. And that number doesn’t include the numerous businesses that are using Facebook via regular accounts rather than Pages.

With all of that going on, Facebook says that the typical user has about 1,500 stories that could show in the News Feed on every visit.

So how does Facebook decide what users see, and what content from Facebook Pages make it into the News Feed? As you can imagine, Facebook isn’t about to give away all the details, but Backstrom did talk openly about several ways that the algorithm has grown up in recent years.

Affinity, Weight & Time Decay

These are “still important,” Backstrom says, but there are now multiple weight levels. “There are a lot of different facets. We have categories and sub-categories of affinity.”

Facebook is attempting to measure how close each user is to friends and Pages, but that measurement isn’t just based on personal interactions. Backstrom says Facebook looks at global interactions, too, and those can outweigh personal interactions if the signal is strong enough.

“For example, if we show an update to 100 users, but only a couple of them interact with it, we may not show it in your News Feed. But if a lot of people are interacting with it, we might decide to show it to you, too.”

Relationship Settings

Another factor is the relationship settings that Facebook users can apply. With each friend, you can go a step further and label the person a “close friend” or “acquaintance.” With liked Pages, users can choose to “Get notifications” or “Receive updates,” and there are deeper settings to control what kind of content the user wants to see.

“We try to extract affinity naturally,” Backstrom says, “but if you go to the trouble to tell us more about your relationships, we will factor that in.”

Post Types

The News Feed algorithm takes into account the type of posts that each user tends to like. Users that often interact with photo posts are more likely to see more photo posts in the News Feed, and users that tend to click more on links will see more posts with links.

Backstrom says this is also applied on a deeper level. “It’s not just about global interactions. We also look at what types of posts you interact with the most from each friend.”

In other words, Facebook Page owners that continually publish one type of post are likely not having those posts seen by fans that interact with other types of posts.

Hide Post / Spam Reporting

News Feed visibility can also be impacted by users’ ability to hide posts or mark them as spam. But it’s not as simple as having a set threshold that will cause posts to stop showing in users’ News Feeds.

“For every story, we do the same computation,” Backstrom explains. “Given this story, and given the user’s history, what’s the probability that you’ll like this story? What’s the probably that you’ll hide it? We’re looking at this and trying to decide, is it a net positive to show this story in the News Feed?”

Further, Backstrom says there’s an element of decay when considering posts that have been hidden. Recent “hides” may carry more weight when deciding if a post shows in the News Feed, but those “hides” will have less impact as they decay over time.

Clicking On Ads, Viewing Other Timelines

The News Feed algorithm is completely separate from the algorithm that decides what ads to show, when to show ads, and where to show them. But how a user interacts with Facebook ads can influence what shows in the News Feed.

“Nothing is off the table when we’re looking at what we should show users,” Backstrom says. “It can be clicking on ads or looking at other timelines. It doesn’t have to be just what the user interacts with in the News Feed.”

Device & Technical Considerations

Yep, the News Feed algorithm even considers what device is being used and things like the speed of a user’s internet connection when deciding what to show.

“The technical limitations of some old feature phones make it impossible to show some content,” Backstrom. “We also know that some content doesn’t perform as well with Facebook users on certain devices. And if the user has a slow internet connection, we may show more text updates. We’re trying to show users content that they’ll find interesting and want to interact with.”

Story Bumping & Last Actor

Don’t forget these two changes that Facebook just announced last week. Story Bumping bends the “decay” rules by giving older, unseen posts a second chance at News Feed visibility if they’re still getting interaction.

Last Actor puts a premium on recency. Facebook is tracking a user’s most recent 50 interactions and giving them more weight when deciding what to show in the News Feed. This works on a rolling basis, so the value of an interaction will decline after the user has made 50 more recent interactions.

Final Thoughts
It should be clear that Facebook’s News Feed algorithm has developed significantly over the past few years. EdgeRank is a thing of the past, and it’s been replaced by a machine learning-based algorithm that, as Backstrom says, “only ever gets more complicated.”

That poses new challenges for brands and marketers hoping to get attention on Facebook, but the company says its advice to Page owners and others is the same: Create and publish and a variety of interesting content that will attract shares, comments, likes and clicks. That requires understanding your Facebook fans — from the types of posts they interact with to the different devices they might be using when they’re on Facebook.

We’ll keep reporting on Facebook’s News Feed changes, and our contributing writers will keep sharing tips and advice, too. You might also keep an eye on the new Facebook for Business news page because the company has promised to be more open in the future about changes that affect how the News Feed works.

Matt McGee on Marketingland.com

All about EdgeRank

What is EdgeRank?

EdgeRank is the Facebook algorithm that decides which stories appear in each user’s newsfeed. The algorithm hides boring stories, so if your story doesn’t score well, no one will see it.

The first thing someone sees when they log into Facebook is the newsfeed. This is a summary of what’s been happening recently among their friends on Facebook.

Every action their friends take is a potential newsfeed story. Facebook calls these actions “Edges.” That means whenever a friend posts a status update, comments on another status update, tags a photo, joins a fan page, or RSVP’s to an event it generates an “Edge,” and a story about that Edge might show up in the user’s personal newsfeed.

It’d be completely overwhelming if the newsfeed showed all of the possible stories from your friends. So Facebook created an algorithm to predict how interesting each story will be to each user. Facebook calls this algorithm “EdgeRank” because it ranks the edges. Then they filter each user’s newsfeed to only show the top-ranked stories for that particular user.

Why should I care?

Because most of your Facebook fans never see your status updates.

Facebook looks at all possible stories and says “Which story has the highest EdgeRank score? Let’s show it at the top of the user’s newsfeed. Which one has the next highest score? Let’s show it next.” If EdgeRank predicts a particular user will find your status update boring, then your status update will never even be shown to that particular user.

Caveat: There actually appears to be two algorithms, although this has not been conclusively proven. The EdgeRank algorithm ranks stories, and a second algorithm sorts the newsfeed. This newsfeed algorithm includes a randomization element and a keyword aggregator. Zuckerberg mentioned in an interview with TechCrunch that Facebook users found it eery how well Facebook knew what they were interested in, so they started randomizing the newsfeed slightly.

The numbers on this are frightening. In 2007, a Facebook engineer said in an interview that only about 0.2% of eligible stories make it into a user’s newsfeed. That means that your status update is competing with 499 other stories for a single slot in a user’s newsfeed.

How does EdgeRank work?

EdgeRank is like a credit rating: it’s invisible, it’s important, it’s unique to each ur, and no one other than Facebook knows knows exactly how it works.

At Facebook’s 2010 F8 conference, they revealed the three ingredients of the algorithm:

  1. Affinity Score
  2. Edge Weight
  3. Time Decay

Affinity Score

Affinity Score means how “connected” a particular user is to the Edge. For example, I’m friends with my brother on Facebook. In addition, I write frequently on his wall, and we have fifty mutual friends. I have a very high affinity score with my brother, so Facebook knows I’ll probably want to see his status updates.

Facebook calculates affinity score by looking at explicit actions that users take, and factoring in 1) the strength of the action, 2) how close the person who took the action was to you, and 3) how long ago they took the action.

Explicit actions include clicking, liking, commenting, tagging, sharing, and friending. Each of these interactions has a different weight that reflects the effort required for the action–more effort from the user demonstrates more interest in the content. Commenting on something is worth more than merely liking it, which is worth more than merely clicking on it. Passively viewing a status update in your newsfeed does not count toward affinity score unless you interact with it.

Affinity score measures not only my actions, but also my friends’ actions, and their friends’ actions. For example, if I commented on a fan page, it’s worth more than if my friend commented, which is worth more than if a friend of a friend commented. Not all friends’ actions are treated equally. If I click on someone’s status updates and write on their wall regularly, that person’s actions influence my affinity score significantly more than another friend who I tend to ignore.

Lastly, if I used to interact with someone a lot, but less so now, then their influence will start to wane. Technically, Facebook is just multiplying each action by 1/x, where x is the time since the action happened.

Affinity score is one-way. My brother has a different affinity score to me than I have to him. If I write on my brother’s wall, Facebook knows I care about my brother, but doesn’t know if my brother cares about me.

This may sound confusing, but it’s mostly common sense.

Edge Weight

Each category of edges has a different default weight. In plain English, this means that comments are worth more than likes.

Every action that a user takes creates an edge, and each of those edges, except for clicks, creates a potential story. By default, you are more likely to see a story in your newsfeed about me commenting on a fan page than a story about me liking a fan page.

Facebook changes the edge weights to reflect which type of stories they think user will find most engaging. For example, photos and videos have a higher weight than links. Conceivably, this could be adjusted on a per-user level–if Sam tends to comment on photos, and Michelle comments on links, then Sam will have a higher Edge weight for photos and Michelle will have a higher Edge weight for links. It’s not clear if Facebook does this or not.

As a sidenote, Facebook may actually rank the act of commenting, liking, visiting a fan page, or even fanning a page differently depending on the source. For example, becoming a fan via an ad may have a lower Edge score than becoming a fan by searching for the fan page and then becoming a fan. This makes intuitive sense–the one user is hunting for the page and generally will care more about page stories than someone who had an ad thrust in their face. There is no conclusive proof of this though.

New Facebook features generally have a high Edge weight in order to promote the feature to users. For example, when Facebook Places rolled out, check-ins had a very high default weight for a few months and your newsfeed was probably inundated with stories like “John checked into Old Navy.” Generally, after a few weeks or months Facebook dials the new feature back to a more reasonable weight.

Time Decay

As a story gets older, it loses points because it’s “old news.”

EdgeRank is a running score–not a one-time score. When a user logs into Facebook, their newsfeed is populated with edges that have the highest score at that very moment in time. Your status update will only hit the newsfeed if it has a higher score–at that moment in time–than the other possible newsfeed stories.

Facebook is just multiplying the story by 1/x, where x is the time since the action happened. This may be a linear decay function, or it may be exponential–it’s not clear.

Additionally, Facebook seems to be adjusting this time-decay factor based on 1) how long since the user last logged into Facebook, and 2) how frequently the user logs into Facebook. It’s not clear how exactly this works, but my experiments have shown time-decay changes if I log into Facebook more.

How do I check my EdgeRank Score?

Anyone who claims to check your EdgeRank is lying to you. It is completely impossible.

You can measure the effects of EdgeRank by seeing how many people you reached. You can also measure how much engagement you got (which impacts EdgeRank) using a Facebook analytics tool.

But there is no “general EdgeRank score” because each fan has a different affinity score with the page.

Furthermore, Facebook keeps the algorithm a secret, and they’re constantly tweaking it. So the value of comments compared to likes is constantly changing.

Lastly, fan pages never appear in the newsfeed-stories by/about the pages show up. So I really don’t care about the EdgeRank score of the page, I only care about the EdgeRank score of the status update (which is affected by the EdgeRank score of the page).

There will never be a 3rd-party tool that can measure EdgeRank. Too much data is private–eg, if a fan leaves a comment on my page’s status update, I can’t know how tightly he’s connected to the other fans–and the more tightly he’s connected, the more his comment impacts the Affinity Score of the status update for the other fans.

How can I optimize my fan page for EdgeRank?

It’s hard to trick an algorithm into thinking that your content is interesting. It’s much easier to rewrite your content so your fans leave more likes and comments.

Take your stodgy press releases, and turn them into questions that compel your fans to engage.

Here’s some examples:

  • “Click ‘like’ if you’re excited that we just released our iPad app.”
  • “Fill-in-the-blank: All I want for Christmas is ___. Our latest Christmas special is X.”
  • “Yes/No: I brushed my teeth last night. We just announced a new brand of toothpaste.”
  • “On a scale of 1-10, I think Obama is a great president. Watch this video of our CEO shaking hands with Obama.”

All those likes and comments will increase the Affinity Score between each fan and your page, boosting how many fans see your status updates in their newsfeed.

By Edgerank.net (jeff@edgerank.net)