Know How Meta, Instagram & Twitter are Utilizing the Big Benefits of AI in Social Media
"AI has the potential to improve billions of lives, and the biggest risk may be failing to do so. By ensuring it is developed responsibly in a way that benefits everyone, we can inspire future generations to believe in the power of technology as much as I do," - Sundar Pichai, CEO of Google & Alphabet.
Instagram, Twitter, and Facebook have become an integral part of our daily life. In fact, these social media platforms went beyond personal benefits. These three social media platforms are now deciding the fates of business organizations- whether it's a global business giant or just local startups.
But do you know what makes these platforms so eligible and unavoidable for each business organization?
Yes, you sensed it, right? It's nothing but artificial intelligence (AI) and data science.
Today applications of Data science and AI in social media are integral components of social media giants like Instagram, Twitter, and Facebook. All these organizations are utilizing the various benefits of AI enhancing customer engagement, data security, and in-depth analytics.
AI has the potential to transform every function in the industry and has scaled these industries to grow immensely in the social media sphere too.As a result, AI and social media are becoming strongly intertwined.
According to Backlinko, the number of monthly users on Instagram will reach the 1.37 billion mark in 2022. The user activity created an abundance of data. AI-powered analytics is the most reliable tool for scrutinizing all the data generated on this platform.
Instagram, today's most popular social networking app for sharing photos and videos, was launched in 2010. Users interact with each post by showing care and affection with a heart button instead of a like, comments, and hashtag. These activities generate enormous amounts of data.
Optimizing Content Through Application of Data science and AI in Social media
How does Meta (formerly Facebook) make use of AI in social media?
Meta is improving its deep learning methodologies
In contexts with deep learning, Meta (Facebook) employs a variety of techniques, including NLP and neural networks. META's NLP (Natural Language Processing) system's deep learning model uses neural networks to identify spam, inordinate promotional posts, and click baits via sorting and segmentation. Using such techniques to filter the contents and deliver a clean and optimized result to the user's news feed.
Furthermore, Meta has recently launched a deep-text technology that uses neural networks to identify clickbait, promotional posts, and spam. This deep learning model will be a combination of NLP and machine learning. To sort through the posts and comments, Deep Text uses unsupervised learning technology.
What is Deep Text?
Deep Text is a more advanced deep learning concept that has the potential to replace traditional NLP and is capable of understanding text in a variety of languages. Conventional NLP appears to be significantly less effective than Deep Text due to scaling and linguistic issues.
Deep text is currently being tested by Meta on a few Meta (Facebook) experiences. For instance, DeepText uses conversational applied machine learning (AML) in messenger to better understand when a user wants to go somewhere.
Examples:
The primary example of deep text can be understood as if a person wants to go somewhere and just need to type "I just need a ride," the algorithm will display an alternative below the text to select an option like "Uber or Ola to book a ride." Similarly, if the person types "I don't need a ride," it will automatically suggest through its algorithm and stop displaying the links for the riding option.
A multilingual, high-accuracy model is another illustration of META that can help people find the best solutions for their problems. Think about a scenario where someone wants to sell a bike for $200 to an interested buyer only. Deep text will recognize the text's pattern and extract relevant information about the item, encouraging resellers to use their existing resources and streamlining Facebook transactions with ease.
Here we can deduce that DeepText eventually enhances Facebook experiences by comprehending and extracting post intent, sentiment, and entities (events, people, and places), mixing content signals like text and images, and automating the removal of objectionable content like spam.
Celebrities and athletes use Facebook to draw large numbers of comments by selecting the most pertinent words in numerous languages, but maintaining comment quality is challenging. Deeptext also aids in the additional challenge of surfacing the most important or impressive comments.
AI and social media enhancing Twitter
Twitter is constantly experimenting with artificial intelligence in order to provide a great user experience on its social media platform. Here's a breakdown of how Twitter uses AI in its social media platform.
1. Recommendation
If we start from scratch, Twitter's AI algorithm manages all tweets. An AI algorithm aids tweet segmentation and lets users view suggested posts and tweets. In order to determine which tweet is the most pertinent and highly recommended to the audience, Twitter has implemented NLP and a Tweet ranking algorithm.
2. Remove hateful accounts
AI algorithms aid in the identification and removal of accounts that may encourage extremist groups, terrorist activities, or tweets containing hate speech. This is critical in order to keep everybody's online community safe and positive.
3. Image cropping tool
Twitter's use of neural network architecture in its design has elevated its user experience to new heights. This enables the platform to display only the most intriguing part of an image for the thumbnail, increasing the chances of users clicking on the content
Use of AI in social media platform Instagram
Instagram has been using AI for a long time, and thanks to AI and Big data algorithms, every user now has a personalized feed that is designed for specific interests. Below are the
uses of AI in the Instagram platform:-
Suggesting searches from the Instagram algorithm
The Instagram algorithm is intended to rank content on the platform and provide users with easy access to the content they enjoy. To determine how to distribute and design content, the algorithm examines metadata (captions and image alt text), engagement metrics, and hashtags. This ensures that users can see the content they want while also introducing new and interesting content to them.
Instagram algorithm cross-refers information about the content (reels, stories, posts) from the user's data (likes and behavior on the Instagram platform), showing the right content that the specified user likes.
"We want to make the most of your time, and we believe that using technology [the Instagram algorithm] to personalize your experience is the best way to do that," wrote Instagram CEO Adam Mosseri in 2021
How does Instagram content work?
Understanding the algorithm by which Instagram operates is essential in order to maximize content-sharing techniques and generate traffic from the majority of posts.
There are millions of photos, videos, and reels on Instagram's platforms, and the company's user base is steadily expanding. AI created a search function for an extensive database to help users find images relevant to their activity and user experiences. The mechanisms through which Instagram operates are mentioned below.
Working on the Instagram algorithm
Each time the user opens the app, The Instagram algorithm instantly evaluates all the content that is accessible and determines which content needs to be shown in a specific order.
The first three ranking factors of the Instagram algorithm in 2023 are:-
1. Relationship between Creator and audience:-
If you DM, follow, or comment on each other's posts? If certain users seem to interact with you a lot in the past, you're more likely to see their newly posted content frequently. (crucial for businesses: Active community management, which includes responding to D.Ms and comments, which in return improves brand visibility on Instagram).
2. Interest:-
If the user frequently engages with a certain kind of content? When a user likes a particular content item in a different style or format, the Instagram algorithm can detect this and display the same piece of content.
3. Relevancy:-
Instagram's algorithm determines how "Relevant" each piece of content seems to be, such as where it appears to fit into a trending topic and even how timeless it is (recent posts are considered more relevant than old posts).
The secondary Instagram algorithm includes
1. Frequency of using the platform
Instagram will show users the most popular content when the app is opened frequently. This means that businesses could be crowded with customers who are being influenced by what their friends and family are consuming.
2. The number of users a person follows
When a person follows more accounts, the feed becomes more crowded with content from competing accounts.
3. Session time
If an Instagram user spends significantly less time on the app, the user will see posts from friends and family with whom they interact on the Instagram platform. This simplifies the process for businesses to surface their feed.
There are numerous ways to use AI in social media to optimize content, but the aforementioned tips should be known by all social media users because they can be incorporated into social media platforms to improve user experience and viability.
Conclusion
Data science and AI have been changing the way people interact with each other. They are optimizing content and personalizing feeds based on the preferences and dislikes of specific users. These social media apps have been updated with seamless and user-friendly features, allowing users to stay on the app and watch the content they want. Moreover, it has added business features so they can earn money through such apps.
You can work on this above AI feature improving the apps by learning from our live online Data science and AI courses with capstone projects and IBM certification. To stay tuned with the latest happenings in AI and data scene, follow us on Instagram, Twitter, and Facebook.
