How Facebook ranks content in 2023: Feed, Stories, Reels and more

Here's everything we know about how Facebook ranking algorithms work to help you maximize your visibility and engagement.

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Facebook has updated its resource explaining how it ranks content across its website, including Feed, Stories and Reels.

Meta has also announced new tools and features for improved transparency on the social network, which will better assist marketers.

Why we care. Marketers and advertisers need a strong understanding of how Facebook ranks content in order for them to make informed decisions about their campaigns to achieve maximum visibility and engagement.

What’s new? Meta published a number of new features designed to provide greater clarity on its ranking factors via its digital newsroom:

  • System Cards: Facebook has created 14 system cards to help marketers understand how Facebook uses AI to rank content and create feeds that are tailored to individuals. The cards also explain how individuals can control what they see.
  • ‘Why Am I Seeing This?’: Meta is expanding this feature to Facebook Reels in the coming weeks. It enables people to understand how their previous activity on the site has influenced what content AI is currently deeming relevant to them and subsequently serving on their accounts.
  • ‘Show more, show less’: Facebook plans to make this feature, which is currently available on all posts in Feeds, Videos and Reels via the three-dot-menu, more prominent.
  • Meta’s Content Library and API: Facebook plans to roll out a new suite of tools for researchers called Meta’s Content Library and API in the next few weeks. The new library is set to include data from public posts, pages, groups, and events on the social networking site.

System cards

Facebook’s new system cards are the biggest update to its resource center. This system consists of 14 cards:

  • Feed: Facebook uses AI to calculate a relevance score for about 500 posts and then ranks them in descending order. The system is built to show a variety of content in the feed, meaning a user shouldn’t see multiple video posts in a row.
  • Feed Ranked Comments: AI ranks comments in order of what it deems will be most relevant to each user. It does this by examining factors such as how popular other comments are and whether they have been published by someone in their network.
  • Feed Recommendations: AI will determine what content users are most likely to engage with by looking at factors such as groups they have recently joined and posts they have liked. It then uses this information to decide what content (e.g., posts, reels, live videos) to recommend.
  • Reels: AI selects what reels are served and in what order by determining what a user is most likely to be interested in. It makes these predictions by examining factors such as accounts the user has followed, liked or recently engaged with.
  • Stories: The AI system automatically shows Stories from people or pages by predicting what a user most likely to be interested in. The system also applies rules to make sure users are served a balanced mix of content in Stories.
  • People You May Know: AI tried to determine people who may be of interest by looking at factors such as people who are friends with a user’s friends or people that are in the same groups as the user.
  • Video: When users view and interact with Facebook Video, one of the underlying AI systems delivers a range of video types that may match their preferences. This content is found in the Video tab. It might include reels, music, gaming or shows. This is content users may be interested in from creators that they may not follow.
  • Marketplace: When a user views and interacts with Facebook, including Facebook Marketplace feed, one of the underlying AI systems recommends relevant Marketplace listings. For example, users can see items for sale in categories such as home goods, pet supplies and sporting goods. Users’ feeds might also include other recommendations, such as sellers and content that they may be interested in.
  • Notifications: AI chooses what notifications to send and ranks notifications in order of what it deems will be most relevant to the user. Meanwhile, previously viewed notifications are displayed in the order in which they were received.
  • Search: AI awards each potential search result a score relating to how relevant that content is to a user by examining factors such as content type. It will then serve users the results in order of relevancy based on this scoring
  • Groups Feed: AI automatically determines which posts appear in the Groups feed, and in what order, by scoring content by relevancy.
  • Individual Group Feed: AI predicts what content users are most likely to engage with and then ranks it according to relevancy in their individual group feed. Relevancy factors include what and whom users have recently followed, liked or engaged with.
  • Suggested Group: Facebook’s AI will look at factors such as groups a user’s friends are members of and related topics to products a user may have recently engaged with, then use this data to identify other groups that may be of interest.
  • Pages You May Like: AI will suggest pages to follow based on pages a user’s friends have recently liked or pages that might relate to products and posts the user has recently engaged with.

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Meta’s Content Library and API

Another big update to Facebook’s resource center is Meta’s new Content Library and API. The database is set to include data from:

  • Public posts
  • Pages
  • Groups
  • Events

People will be able to use the library to search, explore and filter on a graphical user interface or through a programmatic API.

However, under current guidelines, this tool has been set up specifically for researchers from qualified academic and research institutions pursuing scientific or public interest research topics. In order to gain access to this data, researchers will need to apply.

Personalizing the user experience

Facebook confirmed that in addition to providing greater transparency into its ranking factors, it also wanted to give users the tools to take back control of the content they see – for example, the ‘Why Am I Seeing This?” feature.

These tools give Facebook users the ability to shape their own experiences, and choose what what they do and don’t want to see. People can make changes by visiting their Feed Preferences on Facebook as well as through Settings.

What has Facebook said? Nick Clegg, Meta’s president of Global Affairs, shared details on the Meta digital newsroom about how AI is ranking content and how it’ll be easier for users to control what they see moving forward. He said:

  • “[Our AI] systems make it more likely that the posts you see are relevant and interesting to you. We’re also making it clearer how you can better control what you see on our apps, as well as testing new controls and making others more accessible. And we’re giving more detailed information for experts so they can better understand and analyze our systems.”
  • “Our AI systems predict how valuable a piece of content might be to you, so we can show it to you sooner. For example, sharing a post is often an indicator that you found that post to be interesting, so predicting that you will share a post is one factor our systems take into account.”
  • “As you might imagine, no single prediction is a perfect gauge of whether a post is valuable to you. So we use a wide variety of predictions in combination to get as close as possible to the right content, including some based on behavior and some based on user feedback received through surveys.”
  • “We hope by introducing these products to researchers early in the development process, we can receive constructive feedback to ensure we’re building the best possible tools to meet their needs”.

Deep dive: You can find a more detailed explanation of the AI behind content recommendations on the Meta AI blog. For more information on how AI uses signals to make predictions, you can visit Meta’s  Transparency Center.


About the author

Nicola Agius
Staff
Nicola Agius is Paid Media Editor of Search Engine Land after joining in 2023. She covers paid media, retail media and more. Prior to this, she was SEO Director at Jungle Creations (2020-2023), overseeing the company's editorial strategy for multiple websites. She has over 15 years of experience in journalism and has previously worked at OK! Magazine (2010-2014), Mail Online (2014-2015), Mirror (2015-2017), Digital Spy (2017-2018) and The Sun (2018-2020). She also previously teamed up with SEO agency Blue Array to co-author Amazon bestselling book Mastering In-House SEO.

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