The Extraordinary Applications of Big Data and AI in Instagram 



Data science is becoming a powerful force on social media. Today, Instagram is an increasingly popular social media platform with over 1 billion active users. With that many people, it stands to reason that there would be a lot of data available on the platform. 

For example, when you search for a hashtag on Instagram, you can now get suggestions based on your interests and past interactions with people who use that tag. This means that you’ll be able to find more relevant accounts, allowing you to engage with them better and their content.


In addition, Instagram is also making it easier for users to follow posts from specific accounts by letting them choose how many posts they want to see in a row—this helps avoid scrolling fatigue and gives users more time to interact with images before moving on to another one.


Data Science in Instagram – How does it help?


Instagram was the first to come up with a bespoke solution for this type of user behavior. They call the system ‘like’ optimization, based on an algorithm that predicts what a user might like. It recommends other accounts based on one’s past browsing patterns. The aim is to tailor each person’s feed to their interests through intelligent content filtering. As yet, this is the most effective way of increasing engagement.


There are other ways data science is helping Instagram grow: 


  • Explore and Search Feature


Explore and search are two of the most popular tools on Instagram. Accounts are searched for by people looking for what they enjoy and discovering new accounts with relevant interests. With this feature, you’ll be able to learn about the most popular tags, information, and material. Instagram trends are a collection of the most popular data and activities that have been gathered from all around the world using artificial intelligence and big data. In addition to search and tagging facilities, it also supports hundreds of users uploading and tagging pictures.


How does this work?


Instagram uses a machine learning approach known as “word embedding” to identify accounts that are similar to one another. By using this method, it is possible to determine how closely linked words are in a text by analyzing the sequence in which they are used. Instagram uses the same algorithmic approach when determining how closely two accounts are linked.


Since users have previously liked or saved content from “seed accounts,” the Explore algorithm uses this information to build its suggestions. Five hundred pieces of material are then retrieved from the accounts that are most comparable to these.


These content pieces are then screened to eliminate all spam, misleading, and policy-violating information. The remaining postings are ordered according to how likely a user is to interact with each one. When everything is said and done, the user’s Explore tab is then populated with the user’s top 25 most popular posts. To know more about word embedding in detail, refer to the machine learning course in Canada, co-powered by IBM.


  • Emphasize Advertising 


Targeting advertising has already become seamless, thanks to Instagram users who express an interest in goods and brands. Instagram guarantees that the big data it creates is used entirely to its advantage by collecting and evaluating the customer insights it obtains from it.

Since it recognizes and understands its users’ search preferences and engagement data, the platform can offer advertising space to businesses looking to reach a specific target group.

Since Meta owns Instagram, the company has access to a wealth of data on its users, including what they enjoy, who they follow and engage with, and the posts they save.


  • Increasing engagement with Instagram bots


Over the years, Instagram has remained popular because of its ability to engage and interact. Instagram Bots are one of the features that have made interacting with one another on the app so simple.


The purpose of these bots is to make it easier for users to engage with their accounts. They may do everything from liking other people’s comments to leaving their own.

Using these bots, the algorithm can predict who is most likely to engage with a specific type of content. Users’ activities can be efficiently automated with this method.


  • Improve User Experience


Instagram must show users the content they’ll be interested in if it wants to keep them getting back. Discovering content that each user will find relevant gets exceedingly challenging as the volume of content increases. In the wake of Instagram’s switch to a historical feed, machine-learning algorithms were brought to help filter the content and learn what is most valuable and relevant to each user over time to produce a personalized feed.


  • Spam Detection


With a plethora of content being posted daily on the app, some of it is likely to be spam.

Instagram’s spam filter is powered by artificial intelligence. Spam messages can be removed from accounts written in nine different languages, including English, Chinese, Russian, Arabic, and others. As soon as a message is identified, it is promptly removed.


Messages are reviewed by AI and DeepText, which determine whether or not the content and context are acceptable for the intended audience. Such context is automatically removed by the technology whenever it arises. Everyone’s current message or context is unique, but the algorithms are the same.


  • Feed Personalization


It is becoming increasingly crucial for the app to serve content relevant to its users as the volume of shared content increases. Because of this, Instagram changed its feed in 2016 to display posts in reverse chronological order rather than the content it expects its users would like and share.

So the machine learning algorithm was set to work to scan through all the information and thoroughly understand which content would be most important to its viewers to build a personalized feed for every one of them. 


  • Blocking Offensive Contents


There has been an increase in cyberbullying and posting objectionable information via social media. According to recent data, approximately 42% of young people report being the target of cyberbullying on Instagram.

As far as offensive content is concerned, it’s becoming the norm on most social networking sites. As a result, Instagram has recognized artificial intelligence (AI) and big data as effective techniques for combating the spread of these vices on their service.


The Instagram algorithm can instantly identify illegal content using Big Data and AI. When this occurs, Instagram will get an immediate notification to block or remove the post.

This is a great way to make IG a more welcoming place for everyone while also increasing the overall quality of the user experience.




In the end, data science is critical to Instagram’s success and will continue to be so for many years to come. The production of value for the company and its users relies entirely on the efforts of data scientists. And as this field continues to grow and develop in novel ways, we can be sure that Instagram is with it. The two are intrinsically linked, bringing value to each other through their relationship. It’s been a long road from the early days of Instagram as a simple photo-sharing app, but data science played an integral role in getting it so far. 

I hope you liked this article on the use of data science in instagram. If you’re a data science enthusiast, join India’s best domain-specialized data science course in Canada, and upgrade your AI and big data skills. Learn how various industries use data science tools to stay ahead of the game. 

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