Billions of people around the world have made social media a part of their everyday lives. Today, Facebook, Instagram, Twitter and TikTok are platforms where users consume content, connect with people and post their updates. Social media creates so much content every second, and artificial intelligence (AI) is used to curate and deliver the most relevant content to each user.
The Evolution of Social Media Feeds
In the early days of social media, posts on a user’s feed were served up in chronological order. That meant that the most recent posts of people and pages you follow would come first. However, as social media became more popular, the amount of content produced became exponentially more. Too many posts crammed together made chronological feeds overwhelming, and soon enough, feeds started to become chronological.
Social platforms introduced algorithmic feeds to improve user experience, making these sites report what they think would be most interesting and engaging to the users at any particular time. The shift to using AI to personalize your feeds was a game changer for how to consume information online.
How AI Personalizes Social Media Feeds
Sophisticated AI systems power the personalized feeds on social media platforms today. By processing mountains of user data, predictive analytics, machine learning, and deep learning algorithms determine what content a user is likely to interact with. Several factors influence the ranking and ordering of posts that AI determines each user sees.
Identity and Interests
Every major social media platform collects data points about identity and interests to understand their users as individuals. Details like age, gender identity, location, pages followed, posts interacted with, groups joined, and browsing habits outside their platforms are analyzed. This data trains algorithms to predict content that aligns with user interests and preferences. While platforms retain this data, users can manage their digital footprint – for instance, you can delete Twitter tweets with TweetDelete quickly and easily.
Engagement
What users are served next also depends on their level of engagement with certain types of posts. If users like, comment on, share or save certain posts, it means they want to see more of that content, and that’s a signal to AI. These are key inputs for how platforms rank their content.
Recency and Relevance
Social content ranking is based on identity, interests, recency, relevance, and AI. Posts from recently followed pages and trending topics get priority. An old post that suddenly becomes relevant can still bubble up to the top, though.
Network and Popularity
The position of a post on users’ feeds also depends on who created a post and how many interactions it received. People who are directly connected or popular influencers will boost their posts higher. In addition, content that gets lots of engagement overall ranks higher.
Benefits of Personalized Social Media Feeds
AI-powered personalized feeds offer a number of benefits for social media platforms and users:
More Engaging Experience
Personalized feeds encourage users to engage more actively on platforms by surfacing the social posts that are most likely to elicit a response from each user. This results in more time on the app, more content consumed, and more user-generated data collected.
Increased Ad Relevance
As they improve ad targeting, the same personalization algorithms apply. This is because platforms can show ads that users are more likely to click on if they better understand user interests and habits. More revenue comes from higher ad relevance.
Discovers New Interests
Personalized feeds also mean users can use what they already like to discover new things they might be interested in. This enables fans to connect at a deeper level with what interests them.
Mitigates Information Overload
Today, there is a lot of social content, and users are overwhelmed. Personalized feeds remove the problem by showing the users the social posts that are most relevant to them and hiding the redundant or irrelevant content.
The AI Behind Personalization Algorithms
Designing and optimizing the algorithms that personalize billions of social media feeds is complex. It involves specialized AI technologies working together:
Machine learning. Classification algorithms are machine learning models trained on huge sets of labeled social data used to assign posts, pages, and users into topics and interest categories. This powers relevance-based ranking.
Deep learning. Deep neural networks uncover complex patterns in social data, such as clicks, comments, shares, and captions. This captures nuanced interests and sentiments to model engagement prediction.
Reinforcement learning. Optimization algorithms are reinforced based on real-time social engagement signals. Recommendation policies evolve dynamically to maximize overall user engagement on platforms.
Natural language processing (NLP) techniques, such as sentiment analysis, entity extraction, topic modeling, and others, extract interests and intent from unstructured text, captions, and comments. This connects user affinity to content semantics.
Graph analysis. Mapping social connections, interactions, co-occurrences, and centrality identifies influential creators, communities, and trends to watch. These graph insights inform ranking decisions.
Challenges in Feed Personalization
Despite its benefits, certain issues plague AI-driven social feed personalization:
Filter bubbles. When recommendations become too narrow, users get restricted within “filter bubbles” of a limited worldview. This can propagate misinformation and polarized opinions.
Clickbait promotion. Engagement-based ranking rewards clickbait headlines and shocking content, crowding out thoughtful discourse in favor of attention-grabbing posts.
Addictive technology. Personalized feeds designed to maximize time spent can foster social media addiction. Long hours of passive scrolling impact mental health and productivity.
Privacy violations. Collecting extensive personal data to fuel accurate recommendations raises user privacy concerns over how much is tracked cross-device and how it is handled.
Algorithmic bias. Insufficient data, non-inclusive training, or proxy metrics for interest can propagate gender, racial, age-based, and other biases in AI systems that influence social recommendations.
The Future of Personalization in Social Feeds
Major platforms now openly acknowledge the negative impacts of personalized feeds designed solely to capture attention and data. Addressing these issues while retaining relevance will be key as social networks evolve their approaches:
Transparent systems. Clearly communicating how recommendations work and giving users controls like toggling personalization on/off can help build trust.
Value-based ranking. Incorporating measures of post “quality” from credibility indicators and community ratings can prioritize valuable discourse over engagement alone.
Diversified exposure. Suggesting accounts, topics, and views beyond user bubbles can preserve an open information environment.
Wellbeing guardrails. Features like reminders to take a break after scrolling for a long time, options to hide “likes,” positive notifications, and other nudges can promote healthy usage habits.
Federated learning. Decentralizing user data processing from social platforms to personal devices provides personalized recommendations securely without exposing private information.
User-controlled identity. Separating immutable aspects of identity from interest graph data gives users more control over how their profile exists in personalized systems.
The AI Behind It All
The personalized social media feeds used by billions every day are powered by sophisticated artificial intelligence. Platforms blend predictive analytics, machine learning and real-time optimization to surface relevant content to diverse users.
But there’s a risk to unchecked personalization: filter bubbles and addiction. With social networks gradually incorporating AI, a balanced approach to recommendations that is both ethical and good for users’ well-being will be essential to the core value. A path forward is transparency, user controls, and human-centric design.
