The popularity of AI's potential to transform the way industries function has grown immensely in the social media sphere as well. It has grown to become an integral component of social media giants such as Instagram, Facebook, and Twitter. These companies are availing the various benefits of AI like enhanced security, better customer engagement, and in-depth analytics.
As per Statista, the number of monthly users on Instagram crossed the 1 billion mark in 2018. All of this user activity has created an abundance of data. AI-powered analytics is the most reliable tool to scrutinize all the data that this platform generates.
Does Instagram Use AI?
The priority of any digital company that wishes to reach the top of the business pyramid is to leverage AI to enhance customer satisfaction. In a similar vein, Instagram AI uses all the data aggregated from user interaction to examine user behavior. With such insights from artificial intelligence Instagram can enhance the user experience and engagement.
How Instagram's Algorithms Enhance the User Experience
There is a widespread misconception that there is one unified algorithm behind AI in Instagram for its overall user experience. In reality, a suite of Machine Learning (ML) algorithms, classifiers, and procedures are responsible for optimizing the app's user engagement. For the average user of Instagram machine learning algorithms are way above their understanding. How Instagram uses AI is something that a technically inclined mind would find fascinating.
So this is how it works. Instagram machine learning algorithms can parse through the extensive business intel and usage-based insights collected from customer usage statistics. Developers at Instagram are continually tweaking these algorithms so that users get to see what they care about the most.
There are custom Instagram machine learning algorithms that guide what content is featured on each page for each user. The Instagram Feed, Explore, Stories and Reels, each function differently based on tailor-made algorithms.
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Different Ranking Systems for Instagram Posts
People have certain expectations for the type of content that each part of the app presents to them. They expect to see content from friends and relatives in their Stories, while the Explore page is meant for discovering quality content from users they do not follow.
The Instagram machine learning algorithms use certain user activities for insights that Instagram refers to as "signals". Signals include what a person posted, when and how often a person posts content, user preferences towards particular types of content, etc.
These signals are used in ranking systems for Instagram posts like the Home Feed Ranking System and Explore Ranking System.
The most essential signals across Feeds, Stories and Explore are defined below:
i)Data around the post: These signals are related to the popularity of a post - how many people are liking the post and how quickly, the location attached to it, and so on. Information around the post matters much more for the Explore page than for the Feed.
ii)Data around the person who posted: How many times the user may have interacted with the person who posted is one important signal used by Instagram.
iii)User's activity: This refers to the user's essential interaction with the app including how many posts you have liked or posted, how many hours you spend on particular posts and pages.
iv)History of interaction with the person who posted: How many times people, in general, have interacted with posts on that person's page and also if you commented or liked their posts.
Using these signals aggregated by artificial intelligence Instagram ranks posts and pages on Explore, Reels, Feed, and Stories. When it comes to Feeds and Stories, the Instagram machine learning algorithm collects the most recent pictures and videos shared by friends as the starting data set. Using all the above signals, the posts are ranked and the higher ranked posts are seen by the user first.
For the Explore page, the users' recently followed profiles and pages, the places visited, posts where comments were posted, are all collected. Based on this data set, AI in Instagram uses predictive analytics to show the user the kind of posts that they are more likely to engage with.
User Engagement Graph for Suggested Posts
A user's activities on Instagram help the ML algorithms build a graph of their interests, which is known as the User Engagement Graph. Each node in the graph represents content that the user has shown explicit interest towards. These nodes also represent "seed" accounts or accounts that the users have interacted with by liking or commenting on their posts.
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These seed accounts are fed as input into a K-Nearest Neighbor (KNN) algorithm, which is one of the simplest ML algorithms. A KNN algorithm helps find the most frequent occurrences or averages of an element in a layered data set.
Using the KNN algorithm, Instagram can predict what the user may be interested in and suggest appropriate posts to them. There are two ML principles governing how these KNN algorithms pick similar posts for a user - Embeddings-based similarity, and Co-occurrence based similarity.
Both these techniques reveal the sequence in which words appear in the text in order to gauge how related they are. Instagram uses the same techniques to decipher and understand how connected any two accounts are to each other. This helps find the right account, post, or page and suggests them to the user to keep them engaged on the app.
Clearing Spam and Surfacing Relevant Content
Instagram utilizes the power of Facebook's AI algorithm DeepText, a Deep Learning (DL) based text understanding engine, to provide users with a spam-free experience. DeepText can also extract sentiments and intentions behind the text to differentiate between comments generated by bots and users' comments. It goes through several thousand comments and pieces of text every second.
This AI technology automates the removal of spam comments. It helps a lot of influencers and public figures avoid objectionable comments when starting public conversations in Live Rooms as well. Instagram officials are developing highly accurate multi-language models of this AI to surface the most relevant comments for the users across geographies.
Eliminating Cyber Bullying
As per a survey conducted by Ditch The Label in 2017, 45% of UK youths between the ages of 12 and 25 reported some form of cyberbullying on Instagram. To reduce this problem, Instagram utilized the vast potential offered by the DeepText AI algorithm's understanding of textual context.
DeepText AI walks a thin line in the eradication of cyberbullying - it must get rid of offensive content without infringing upon free speech. Developers are continuously feeding the AI with a variety of textual content to help it learn the nuances of offensive human remarks.
In late 2019, Instagram had also announced that it would enable the AI to analyze images, in addition to the text, for targeting inappropriate cyberbullying posts.
How Users Influence What They See
How a user interacts with the various parts of Instagram, hugely impacts the content surfaced to the user. Simply by liking posts, commenting, and visiting account pages, you can decide the content you'd like to see on your Feeds, Stories, Reels, and Explore pages. Here are some ways that Instagram lets you do so:
i) Select Close Friends: There is an option to select Close Friends who would be able to see your Stories while barring others from seeing them. Instagram's ML algorithms use this information to prioritize posts that you get in your Feed to be from these friends.
ii)Muting and Reporting: Accounts muted and reported by you are sent to the top of the pile, while DeepText AI parses through offensive content. Not only do you stop seeing this content, but Instagram recommended these pages to moderators to consider removing or freezing them.
iii)The "Not Interested" Option: If you choose to press the Not Interested button when Instagram recommends something to you, the app uses this information to optimize recommendations in the future. The KNN algorithm will sharpen the recommendations it makes to you as well as to the people you follow and your followers.
Do You Wish to Leverage AI Capabilities in your Mobile App?
Daffodil has an extensive catalog of Artificial Intelligence enablements to drive your mobile app's customer engagement. We develop systems that use Machine Learning to interpret complex data and identify trends to provide the right insights for your product.
You can learn about the potential of AI through various use cases in our blog. Additionally, you can learn about Daffodil's AI Application Development services offered to opt for the best solution that is suitable for your product.