Social media has become a massive component of our digital economy, using it to connect with customers and reinforce their brands across the world. Platforms like Facebook, YouTube, and TikTok are creating new businesses for content creation, providing audiences and ad spaces.

With over 4 billion active social media users around the world, Artificial Intelligence (AI) plays a critical role in maintaining these platforms and managing the massive amounts of data being generated. So let's take a closer look at how Deep Learning is helping social media expand.

The Evolution of Social Media Platforms

The earliest iterations of social media began in the 1990s when the internet was first made publicly available. Websites with forums or bulletin board systems gave early internet users the ability to leave text-based messages with time signatures so that conversations could be held to some degree. 

By the mid-2000s, internet use had steadily increased and more casual users began flocking to new websites like Myspace and Friendster that gave people the opportunity to connect with their IRL friends and family, sharing content and posting about themselves and their interests. This shift in user engagement was called Web 2.0 and was driven by the various forms of new content that could be uploaded and shared online more easily like videos and pictures that quickly went viral. 

Over the next two decades, social media platforms would refine themselves and establish new tech titans like Meta and Twitter that acquired and connected the billions of users we see today. Now, users are connecting with more than just their friends and family, but with people around the world with common interests or shared business needs. 

How AI Powers Social Media Algorithms

With Social Media’s massive scale and digital location, AI has become the perfect tool for managing the wealth of data being generated daily by users. Once captured by algorithms, this data can be manipulated to provide key insights on consumer behavior or recommendation feeds that keep users engaged longer. 

Personalized content feeds like YouTube’s recommendation algorithms help direct users to new content that they will likely find enjoyable based on previous viewing habits. With more engagement, this helps content creators build their brands and establish new jobs for creatives. 

Data on user activity also benefits marketers who can craft more targeted ads based on viewing habits. For platforms like Facebook and Instagram, this can help businesses promote their products to the most relevant audiences by combining demographics, location, and viewing habits to create big data. 

AI-Driven Content Creation and Curation

AI can aid content creators as well, giving them more advanced tools to improve their production. A few examples include: 

• Editing software: Programs for video and image editing implement convolutional neural networks to help analyze imagery, optimizing visual settings. Many of the programs can now expand background layers too with generative AI. 

• Content strategy: Large language models give creators their own virtual assistant that can help them daft scripts for videos or experiment with new serial content to grow their channels.

• Viewer trends: Big data can also be used by small brands, providing insights into viewer engagement which helps content creators identify what factors influence their most successful material. 

Predicting Trends and Insights for Marketers

The amount of data being generated by social media platforms is gargantuan, with every like, comment, share, upvote, etc., being used to capture information about users and the content they’re interacting with. By using Predictive analytics, marketers can create personalized marketing campaigns that target niche communities and subcultures with preferred products. 

However, this has created some controversy as many people learn more about big data and the amount of information they collect, and share, with other businesses. Data collection has become a growing concern, with many websites now being required to offer opt-out services. 

Combating Misinformation and Ensuring Safety

While Social Media has become the perfect playground for new AI technology, its ability to reinforce online communities by drawing them together with personalized content has created issues with how information is verified. After the 2016 presidential election, the debate over the spread of misinformation on Facebook and Twitter became extreme and was compounded by the Covid-19 pandemic. 

Now, with generative AI becoming more available, the risk of deepfakes is also creating major concerns. Content can be found all over platforms like YouTube of AI being used to replicate the voices and personalities of famous celebrities and politicians like Joe Biden, Donald Trump, Elon Musk, and Joe Rogan. Although many of these videos are made for comedic purposes, they showcase how accurately AI can portray people we recognize and could lead to drastic societal problems if left unchecked. 

The Future of AI in Social Media

AI and social media are both two powerful developments in technology that are impacting daily lives around the world. However, concerns that social media is too easy to manipulate are driving a wedge in society, especially when discussions about sensitive politics and history become involved. 

Many social media platforms are under major scrutiny and their use of AI is a part of the debate. While there are many exciting possibilities for content creators and marketing firms to explore with AI and social media, the general public has begun to lose a lot of interest in social media as companies like Twitter, Reddit, and Meta continue making poor decisions that are ruining the experience for consumers.