How does the Facebook algorithm work for organic posts?

In 2025 Facebook’s algorithm has become an AI-driven discovery engine designed to show content that users are more likely to enjoy and engage with.

This shift is a big change for creators, brands and users, because what we see is now guided less by who we know and more by what Facebook’s AI engine predicts you’ll find engaging.

It also focuses more on short-form video, especially Reels, and favors original, high-quality content that encourages conversation and private sharing.

Showing users content from accounts they don’t follow is also a new approach.

The way Facebook is evaluating content is still a four-step process:

Inventory (all available posts)
Signals (data about the post and user)
Predictions (how likely the user is to engage)
Relevance Score (content ranking system)

The four ranking factors that Facebook uses for content evaluation

The process, which aims to personalise the experience and boost engagement, relies on checking posts, analysing signals, making predictions and giving each post a relevance score.

Despite its complexity, the News Feed algorithm mainly relies on four key ranking factors which I have listed below.

Inventory

The algorithm starts by gathering posts from your friends, the pages you follow, the groups you’re in, ads and recommended posts.

Any content that breaks Facebook’s rules is removed right away.

Signals

Next, the algorithm looks at different signals to decide how relevant each post is.

These include, when it was posted, who shared it, how often the user interacts with them, the type of content (like photos, videos, or links), how the user engages with similar posts, their local time and even their internet speed.

Predictions

Based on the signals, the algorithm predicts what content will matter most to each user.

For example, if someone usually engages with brand posts in the morning, the algorithm will show more of those at that time.

Relevance

Finally, each post is given a “relevance score”. Higher-scoring posts appear first in the Feed.

The algorithm also adds recommended content, making sure not to show too many posts from the same creator or similar types in a row.

Key factors influencing relevance

User’s past interactions with similar content
Total viewing time on previous related posts (videos, stories, Reels)
Content quality, originality and format variety
Engagement likelihood such as comments, shares and reactions
Signals for authenticity, authentic conversations, meaningful interaction, not just passive scrolling consumption
Compliance with platform rules such as posts flagged for clickbait, misinformation, or low-quality links are down-ranked

Customising the Feed

Users can also influence the algorithm and their own feed by managing favorites, using “Show more/Show less” and by hiding, snoozing, unfollowing or reporting posts.