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Have you ever wondered why certain types of posts appear at the top and recur frequently the moment you open Facebook? You might see a post from one friend immediately, while another’s doesn't show up for days. The same happens on Instagram; a Reel from an unknown creator—whom you don't even follow—might suddenly pop up on your screen. Meta’s algorithm is responsible for all of this. Now, this very algorithm is in the spotlight for briefly removing a post by Prime Minister Narendra Modi. Meta has admitted to accidentally removing a video posted by the Prime Minister on July 23, 2026, and has apologized. This is not the first time Meta’s algorithm has been embroiled in controversy; it has previously faced allegations of suppressing or boosting specific content.

Let’s return to the core question: what exactly is the Meta algorithm, and how does it work? Simply put, it is a system that sifts through millions of posts every second to determine what appears first on your screen. Meta doesn't rely on just one algorithm; it uses several. Different algorithms power Facebook and Instagram, and there are distinct ones for posts, Reels, the Explore page, and Threads. However, they all share a single objective: to show you the content you are most likely to engage with.

How does the Meta algorithm work?

Meta’s algorithm doesn't operate like magic; rather, it is a lightning-fast, intelligent, AI-driven "recommendation engine." It closely tracks every click, like, share, save, and second of watch time. It learns from these interactions and uses that data to decide which post should appear first on your screen next.

The help page on the Facebook.com website explains how Meta handles content distribution. First, Meta gathers all the posts. Suppose you open Facebook at 8 AM. As soon as the app opens, Meta compiles a list of all the posts that could potentially be shown to you.

This list includes:

Posts from your friends
Pages you follow
Groups you belong to
Sponsored posts
AI-recommended posts
Reels
Viral videos

Meta calls this "Inventory." Think of it like a hotel preparing its menu; Meta similarly prepares a full menu of posts.

**Evaluation**
After creating the inventory, Meta evaluates each post. This is where the algorithm's real work begins. It examines thousands of signals associated with each post, such as:
Who posted it?
How close is that person to the user?
How old is the post?
How many people liked it?
How many people shared it?
How many people commented on it?

Meta also considers whether you are using Facebook or Instagram on a mobile phone or a laptop, the time of day, and your internet speed. It also tracks the type of posts you have recently viewed; essentially, every detail—big or small—serves as a signal for the algorithm to make predictions.

**AI Makes Predictions**
Meta doesn't try to read your mind through its algorithm; instead, it analyzes your past habits to make predictions. Suppose you like cricket: you watch cricket Reels daily, like and share cricket-related posts, and frequently watch match videos. If someone posts a new Reel featuring Virat Kohli or Shubman Gill, Meta can predict that you are likely to watch it in its entirety. Similarly, if you read stock market news, Meta will increase the amount of such content in your feed. If the AI ​​thinks you will like a particular Reel, it will push it to your feed, even if you have never followed that creator.

AI makes predictions for every post.
The AI ​​makes predictions for every post. It considers whether you are likely to like, share, or comment on a specific post, or whether you might watch a video in its entirety or save the post. Meta itself does not know the answers to these questions beforehand, but the AI ​​calculates the probabilities.

Relevance Score
Based on these predictions, Meta assigns a score to every post; this is known as the "Relevance Score." Posts with higher scores appear at the top of your feed, while those with lower scores are pushed down or may not appear at all. The score is determined based on specific parameters—for instance, if there is a 90% probability that you will watch a video to the end or a 40% chance that you will like it, the post's score increases. In other words, for every post...

Disclaimer: This content has been sourced and edited from Amar Ujala. While we have made modifications for clarity and presentation, the original content belongs to its respective authors and website. We do not claim ownership of the content.