Facebook EdgeRank and 3 steps to optimize visibility in the Facebook News Feed (NFO)


The Facebook News Feed contains a selection of highlights from Facebook friends, pages to which a person is connected on Facebook and, to a limited degree, groups. The news feed receives significant visibility in Facebook due to its prime location in the central column of a Facebook user’s home page and its ongoing updates. The News Feed content consists of news and posts from friends and pages, friend requests, tagged photos & notes, responses to event invitations and notifications of group memberships.

The News feed is actually divided into two streams: Top News and Most Recent. The Top News stream, the default, contains selected highlights deemed by Facebook to be the most interesting and relevant, while the most recent stream, visible by clicking on a link, contains almost all of the current activity of friends and pages. It seems that about half of Facebook users click to view the most recent news.  The Most Recent updates are limited to activity from 250 pages and friends but you can remove this constraint by choosing the Edit option at the bottom of the page.

EdgeRank, the algorithm that determines news visibility on Facebook

Facebook News Feed EdgeRank Algorithm
Figure 1: The algorithm EdgeRank for news on Facebook, maybe …

Companies and organizations must convince people to Like them in order that page news may appear in a user’s news feed. Unfortunately collecting likes isn’t enough. With all the news generated by more than 600 million users, 3 million pages1, 1 billion+ groups2 and 3.5 million events per month, only a small percentage of created news will be published in a person’s news stream: back in 2007 it was only 0.2%3 and it is doubtful that things have gotten any better since. The competition to appear in people’s streams is very high. For businesses, careful attention to the factors that Facebook uses, or could use, to promote news and activity highlights in a user’s news stream is critical to the success of their Facebook marketing and communication efforts.

The factors used in Facebook News Feed ranking, according to Facebook

Facebook apparently tells us what their news feed ranking factors are (take that Google!), although it is easy to imagine that things are more complicated than what is said publicly. The algorithm, called EdgeRank, for news feed, according to Facebook:

The News Feed algorithm bases this on a few factors: how many friends are commenting on a certain piece of content, who posted the content, and what type of content it is (e.g. photo, video, or status update).

There are many cases where the Facebook online help is arguably too concise and the above description is as notable for what isn’t said: for example there’s no mention of two significant engagement signals: Likes and clicks on links. Indeed Facebook has said on other occasions that they do use Likes and link clicks, at least experimentally. The following table lists factors that determine ranking of items in news feeds or could influence news feed ranking on Facebook.

ElementTopicDescription
Fresh news and activitiesRecencyRecency of activity (news, tags, comments, likes, link clicks…)
The type of contentContentStatus updates, photos, videos, comments …
Who has published the content itemAuthorIt may be a person, a page, a group, an application. Each will have its own authority and affinity.
Comments from friendsEngagement (edge)The greater the number of comments from friends, the more likely that a story will appear.
LikesEngagement (edge)Counts could be used, particularly those made recently, possibly calibrated on the basis of the authority of who has done the liking and / or the rate of liking
Clicks on links, if presentEngagement (edge)Facebook tracks all links to external sites. It is easy to imagine that this data is used as a news feed item ranking signal.
Personal behavior historyEngagementA user has historically shown greater interest in news by a certain author or of a certain type?

Edgerank Facebook ranking Factors, unveiled

Simple, no? Maybe it’s not so simple. There are, however, two certainties:

  1. Facebook really wants the more relevant, interesting and exciting news & activity, seen from the user’s point of view, to appear. The news feed is a key element in a strategy to encourage people to pass lots of time in front of Facebook, at least until they click on a Facebook ad.
  2. Facebook is a dynamic company with a constantly evolving product. As such, we can set up tests to identify the key news feed ranking algorithm factors and their respective weighting in the Facebook news ranking 2. algorithm, but we won’t be able to count on the algorithm remaining the same for very long. Facebook could and will change the algorithm from one hour to the next.

The life cycle of a news item

Each news item will have a life cycle: the weight and combination of factors needed to get it published in a user’s news feed will vary depending on where the news item is in its life cycle. For example, a newly created news item can not have any comments or likes – the fact that the news is just 0 seconds old will weigh strongly in its favor compared to a news item created 30 minutes ago which must compensate for its age through a high rate of engagement. In the Facebook system engagement is measured by comments and likes added by friends. It is also likely that at a certain point a news story, despite how many comments or Likes friends have added, is no longer a candidate for news feed publication – its too
old, expired. A comment, positive or negative, is most likely worth more than a Like, as it takes little effort, physical and psychological, to click on a Like button. This is one of those cases where an expression of appreciation, a Like, is probably worth less than a negative comment, which requires greater engagement.

…there is only one thing in the world worse than being talked about, and that is not being talked about – Oscar Wilde, The Picture of Dorian Gray

News Feed item types

Facebook has several types of information to promote in news feed streams and it seems that not all types of news are equal. A user may have demonstrated, through likes and / or comments, a preference for certain types of content in the past, such as changes in friends relationship statuses. It is also possible that Facebook gives a priority to certain types of information based on internal Facebook preferences to better highlight part of the site and / or functionality, perhaps even for a limited time.

Author affinity

Author affinity breaks down into several elements, not all of which are necessarily obvious. When Facebook speaks of affinity, they most likely mean considerations such as how many friends are shared between two people, how long two have been friends relative to the length of their common presence on Facebook and perhaps other factors such as common demographics (age, location, gender, languages spoken and / or Facebook interface language) and affiliations (groups, pages, shared networks). Facebook also displays news from third parties if there are comments and / or likes from direct friends, assuming compatible with privacy settings.

Bacon, eggs and spam, spam, spam

To combat spam, annoying and unwanted news, Facebook has a system whereby a user can flag a news item as spam. The same system allows a user to hide all future news from a user, page or application. It is likely that spam reports or requests to hide information from a certain source will decrease an author’s authority. Some factors may increase an author’s authority, such as seniority on Facebook, their number of friends (for pages: Likes), the rate of friend growth (too rapid growth may be considered suspicious) and / or a low rate of abandonment of friends / dis-Likes.

Three steps to optimize an item for News Feed visibility (NFO)

It is tempting to want to know the exact details of a ranking algorithm in order to maximize optimization efforts. Instead hunting down the gory details of an algorithm, it is probably more worthwhile to pay attention to the signals that Facebook has at its disposal, weighing each according to its value in helping Facebook achieve its likely goals. This same approach applies to search engine optimization, SEO, too.

In practical terms:

  1. Collect as many Likes for a page as possible from an audience interested in what the page offers (qualified Likes).
  2. Publish items to the page’s news feed often, but between frequency and quality, it is better to focus on quality.
  3. Engagement is at the heart of the Facebook system. Ensure each post will encourage comments and likes, usually with a call-to-action. Sometimes its as simple as asking What do you think? at the end of the news.

What to know more about Facebook marketing? We offer a day long course dedicated to Facebook marketing. Contact us for more information or consult the course program (currently just in Italian, course also available in English).

This article was also published in Italian on 7 February 2011.


1http://www.facebook.com/press/info.php?statistics Data was retrieved on 17 April 2010; is not currently shown on the page.
2 Facebook does not currently disclose this information, but Google provides an estimate on the number of groups in its index by using the search syntax inurl:www.facebook.com/group.php. The www is necessary as the Facebook site is duplicated under many local sub-domains, e.g. it-it.facebook.com. This type of Google estimate is very approximate.
3 The figure in 2007 was 1 in 500

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About Sean Carlos

Sean Carlos is a digital marketing consultant & teacher, assisting companies with their Search (SEO + SEA = SEM), Social Media & Digital Media Analytics strategies. Sean first worked with text indexing in 1990 in a project for the Los Angeles County Museum of Art. Since then he worked for Hewlett-Packard Consulting and later as IT Manager of a real estate website before founding Antezeta in 2006. Sean is an official instructor of the Digital Analytics Association and collaborates with the Bocconi University. He is Chairman of the SMX Search and Social Media Conference, 13 & 14 November in Milan. He is also a co-author of the Treccani encyclopedic dictionary of computer science, ICT & digital media. Born in Providence, RI, USA, Sean received Honors in Physics from Bates College, Maine. He speaks English, Italian and German.

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