News Feed Ranking Factor Updates: FB Time Spent Algorithm

Facebook added “Time Spent” as a new deciding factor for news feed post ranking. Earlier, facebook used to use “edge rank algorithm” to rank your content in your friends or follower news-feed. Basically, “edge rank” is being calculated on three factors, They are Affinity, Weight, & Freshness. Though there are dozens of input signals in the algorithm, but it was originally built from 3 components:

  • User Affinity: The User Affinity part of the algorithm in Facebook’s Edge-Rank looks at the relationship and proximity of the user and the content (post/status update).
  • Content Weight: What action was taken by the user on the content.
  • Time-Based Decay Parameter ( Freshness ): New or old. Newer posts tend to hold a higher place than older posts.

Later Facebook switched to machine learning algorithm, which takes more than 1,00,000 factors in an account but experts were saying that still “Edge Rank Algorithm” plays most crucial role in deciding rank for post/updates.

Now Facebook announced “Time Spent Factor” as a new deciding factor. This factor is also important in the context of “FB instant article” feature. Let’s suppose if a person reads the complete article but doesn’t do any action (Like/Share/comment), but in practical he/she engaged with the content, so according to Facebook the “time spent factor” will be right factor to determine the engagement.

They Wrote in their release…

“We’re learning that the time people choose to spend reading or watching content they clicked on from News Feed is an important signal that the story was interesting to them. We are adding another factor to News Feed ranking so that we will now predict how long you spend looking at an article in the Facebook mobile browser or an Instant Article after you have clicked through from News Feed. This update to ranking will take into account how likely you are to click on an article and then spend time reading it. We will not be counting loading time towards this—we will be taking into account time spent reading and watching once the content has fully loaded. We will also be looking at the time spent within a threshold so as not to accidentally treat longer articles preferentially.

With this change, we can better understand which articles might be interesting to you based on how long you and others read them, so you’ll be more likely to see stories you’re interested in reading. This change only factors in the time people spend reading an article regardless of whether that time is spent reading an Instant Article or an article in the mobile web browser.

We’ve also heard from people that they enjoy reading articles from a wide range of publishers, and it can be repetitive if too many articles from the same source are back-to-back in their News Feed. We’ll also be making an update to reduce how often people see several posts in a row from the same source in their News Feed.”

Time Spent Viewing

“Building on this work, we’re learning that the time people choose to spend reading or watching content they clicked on from News Feed is an important signal that the story was interesting to them. We are adding another factor to News Feed ranking so that we will now predict how long you spend looking at an article in the Facebook mobile browser or an Instant Article after you have clicked through from News Feed. This update to ranking will take into account how likely you are to click on an article and then spend time reading it. We will not be counting loading time towards this — we will be taking into account time spent reading and watching once the content has fully loaded. We will also be looking at the time spent within a threshold so as not to accidentally treat longer articles preferentially.

With this change, we can better understand which articles might be interesting to you based on how long you and others read them, so you’ll be more likely to see stories you’re interested in reading. This change only factors in the time people spend reading an article regardless of whether that time is spent reading an Instant Article or an article in the mobile web browser.”

Facebook Time Factor Algorithm, Edge Rank Algorithm, Facebook Time Spent Algorithm,

This algorithm is one step further towards machine learning of human behaviour. Experts are saying that this factor will be equally important as like affinity, weight, and time decay (Freshness).

Anand

I am Passionate "Digital Marketing practitioner". I have the fair amount of experience in the traditional way of marketing & Brand building too.

Since 5 years I am self-learning this tech-based online marketing practice and ideas.

The purpose of this blog to share and receive the information and ideas to my community.

Prior to take formal education in Marketing Management. I studied History and Indian culture.

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