Facebook Research Algorithm: We are well familiar with Facebook. Even we introduced an internet jargon verb, called ‘Facebooking’, which is as much famous as ‘Googling’. What does Facebooking mean? It usually refers the act of logging in and using the Facebook, updating the status, posting the photos, videos, commenting, liking, and so on. Before, this research algorithm, an algorithm known as EdgeRank. Various researches on EdgeRank Facebook algorithms are explained in most of the Dissertation Writing.This EdgeRank will decide what articles or posts should be displayed in a user’s News Feed. Let us see a basic definition of EdgeRank and its parameters.
The simplified version of EdgeRank algorithm is presented as,
ue – User affinity
we–How the content is weighed
de – Time based decay parameter
User Affinity – This part of the algorithm looks the relationship and vicinity of the content and the user (i.e. posting / updating the status)
Content Weight – The actions taken by the user about the content in research about ai.
Time based Decay parameter – Here, the newer posts will be in higher places than the old posts.
Apart from these, there are several parameters that are purely proprietary and hence not available in public.
Why Does EdgeRank Matter?
It is estimated that your post is 40 – 150 X times more likely to reach the readers in the News Feed than your page. 27% of all time spent on FB is spent looking at the News Feed, which is 2,835,000,000 minutes/per day(this does not even including mobile). According to a survey, in the US, people spend more time on Facebook News Feed than (ABC + CNN + yahoo + Fox + Times + MSNBC) six major news sites combined. But only 16% of your fans will see your post. So to improve your page’s EdgeRank, certain rules are followed,
- Keep it simple and short
- Be visual
- Ask opinions, polls
- Post frequently
- Post something relevant but not pushy
- Find best time for posting
New Facebook Research
As of 2011, FB stopped using Edge Rank and started using a machine learning algorithm coined as Facebook Algorithm (Various researches of ML algorithms are explained in most of the PhD Proposal Writing and Thesis Writing). Now a days, Machine Learning is becoming one of the trending PhD Research arena. This algorithm is a collection of calculations FB uses to decide what content you have to see, has a lot of influence and dominance. However, in early 2018, Facebook got shattered a little bit by a Russian ad Scandal. So the team decided to curb this fake news by taking certain actions on its platform. So on Jan 2018, the FB algorithm got changed. This updated algorithm has four things. They are inventory, signals, predictions and score using Python development.
- Inventory – It refers to all available content on FB, such as posts from your friends, family group, or from the pages you have liked.
- Signals – These signals can utilize to choose which content goes out. Some of the criteria includes,
Comments and likes on a person’s status of photo
Shares on Messenger
Replying to comments on a video
- Predictions – This is where Facebook uses your profile and behaviors to decide what to show you on the News Feed.
- Score – It refers to value assign to the content Research Blog which in turn determine the relevancy with user.
How to Outsmart the FB algorithm?
There are 9 strategies to outsmart this algorithm and to promote your business brands.
- Know the best time to post on FB.
- Make a video for your content
- Use discussions or ask opinions before posting links for your brands
- Encourage employees and brand people to post your content
- Prioritize tags and photos over external links
- Make unique post
- Start conversations in FB groups
- Narrow down the target audience
- Invest in paid promotions in FB
Are you in need with reaching more fans on your FB page? It’s simple. Just post smarter, easier and better content. Happy Facebooking!
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