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  • MaryGrace Lerin

Facebook Has Released A New Explainer For Their News Feed Algorithm

Facebook has released a new video explaining how its famed News Feed algorithm works. While it doesn't offer any new insights or recommendations, it does provide a decent overall overview of Facebook's content ranking method and why certain topics acquire more momentum than others based on individual reaction.

According to Facebook:

"Our goal is to make sure you see the posts that are most valuable to you at the top of your Feed every time you open the Facebook app. And because most people have more content in their News Feed than they could possibly browse in one session, we use an algorithm to determine the order of all of the posts you can see."

The video explains the feed ranking method, including the four important factors that Facebook considers when deciding what material to show to each user.

The elements are as follows:

Inventory - This is the starting point, with all the posts in the initial sample set that you might be shown each day. These postings are based on the Pages and people you follow, as well as the material they've shared and interacted with, as well as the groups you belong to and the ad content you're qualified to see on any given day.

Signals - The algorithm then employs a variety of signals to determine the relevance of each post to you. This is determined by your relationship to the person/Page sharing the update, as well as how you've interacted with them in the past. The algorithm also takes into account whether this is a photo, video, or link post, which influences what you see depending on your interaction history (for example, if you watch more video, you'll see more video updates).

Predictions - Based on these factors, the algorithm then generates predictions about your probable interaction with each new post, in an effort to emphasize the most personally relevant material based on these criteria.

Score - Finally, the algorithm will score each post in your content pool in order to rank them, taking into consideration all of these variables. The greater the relevancy score, the more likely it is that the post will show towards the top of your Facebook page.

This is the rudimentary concept of the News Feed, and you can infer how to optimize reach on the platform based on it. The more people who find your material personally relevant and connect with it - whether by viewing, responding, commenting, or sharing - the more probable it is that your content will appear higher in each individual's feed. And the larger the group, the greater your Facebook reach.

Users may further personalize their News Feeds by utilizing features like 'Favorites' to choose the top 30 people and Pages they want to view the most, as well as arranging their feed chronologically using the 'Most Recent' option - however this will revert to the algorithmic feed the next time you log in.

Users may also specify that particular postings are irrelevant to them by clicking on the relevant choices in the three dots menu on each post, according to Facebook. As a result, the material will be ranked lower in the future.

Furthermore, Facebook claims that information that violates its standards - such as hate speech and graphic violence - is frequently removed before any user sees it, while content that is judged objectionable but does not reach the bar for removal is frequently down ranked so that fewer people see it. It's worth noting if you want to push the boundaries.

It's a nice, basic explanation of the foundations of Facebook's feed algorithm, which may help explain why you're seeing what you're seeing in your feed and how the process works for those wanting to improve their Facebook strategy.

Similar explainers have previously been released by Facebook in order to eliminate some of the mystery around its content ranking algorithm. Given this, while this new video doesn't contain any new information, it's still worth seeing to ensure that you have a firm grasp on the fundamentals of the Facebook process.

The new video explainer can be viewed here.

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