Social media algorithms are powered by AI, which is machine learning that incorporates various ranking signals to prioritize and personalize content for every user. Trying to game an algorithm in social media posts is never a good idea, but understanding the most important ranking signals can give you a strategic advantage over your competitors.
What is a social media algorithm?
A social media algorithm is a set of rules and signals that rank content on a social platform. It organizes content on social feeds based on how likely each social media user is to like it and interact with it. A social algorithm’s purpose is to create a good user experience by making individual users’ feeds interesting and engaging. Algorithms are the reason why no two users will see the same social content, even if they follow all the same accounts.
Why are social media algorithms important?
As you can see from our algorithm definition, social media requires algorithms because they determine what you see out of the incredibly vast potential content pool. For brands, this means algorithms influence how likely your content is to be seen, both by your followers and by people who don’t yet follow you. And the algorithms can have exponential power. Social media algorithms are designed to serve up content they think a user will like, based on the history of past ranking signals (more on those later). If the user likes, interacts with, or follows any of these posts or accounts, the algorithm quickly learns to serve up and suggest even more of the same.
How do social media algorithms work?
Understanding how social media algorithms work is like unraveling the intricate threads that govern the digital tapestry of user experiences. These algorithms, like invisible guides, determine the content on our feeds. Let’s explore how social media algorithms function and how social teams can exploit them.
Algorithms search for relevant and valuable content
Algorithmic-driven content curation systems such as the Facebook News Feed and Instagram Feed monitor user behavior, interests, and actions to recommend relevant content. For instance, if you engage with basketball content, you’ll likely see more of it. These algorithms aim to refine content suggestions by adapting and learning from user interactions, ensuring content remains relevant.
Signals and important factors that social algorithms consider
Algorithms collect user signals to match them with relevant content. Key signals include:
1. User engagement:
Likes, shares, and comments indicate that users find the content interesting and relevant.
2. Relevance:
Keywords and hashtags give content context and improve its visibility.
3. Timing and frequency:
Posting consistently and at the right time when your audience is active can boost visibility.
4. Recency:
Newer posts are prioritized over older ones.
5. User interactions:
Accounts, interactions, and link click-through rates signal relevance and content quality.
6. Profile authority:
Follower count, consistency, and engagement impact organic reach.
7. Location:
Algorithms take into account the location and demographics of users when curating content. Content may be promoted to users in similar locations.
8. Content type:
Different content types, like videos, images, and text, are treated based on user interaction. Many platforms favor videos because they’re more engaging.
9. Virality:
Content gaining popularity and shares signals relevance to the algorithm.
10. Watch time:
This is the duration users spend watching videos (IG Reel, YouTube Shorts)
Social media algorithms by platform
Every social media platform has its unique algorithm for content curation and display. These platforms look for specific ranking signals to discern user preferences. Let’s explore some social media platforms and their platform-specific rank signals to keep in mind.
Twitter algorithm –
The Twitter algorithm is an AI-powered algorithm designed to curate content based on the user’s interests, preferences, and past interactions rather than chronologically. According to Twitter, their algorithm looks through about 500 million daily Tweets to display only relevant ones on your timeline. In 2023, the updated Twitter algorithm considers:
Location and language:
Twitter shows you content based on your location, especially in their “Trends for You” section.
User interactions:
Content is recommended based on your activity, including who you follow and interact with.
Engagement level:
Tweet popularity is determined by user engagement metrics such as likes, Retweets, and replies.
Relevancy:
The relevance of a Tweet is based on the keywords used, the user’s interests, and previous interactions.
Recency:
Recent Tweets hold importance, especially in the “Trending Topics” and “What’s Happening” sections.
Profile Reputation Score:
Active profiles engaging with others gain higher visibility; Twitter also uses TweetCred, a PageRank algorithm, to rate a profile’s credibility based on its followers, past activities, safety status, and device usage.
Similar accounts:
Profiles are categorized based on the niche or topic discussed.
Facebook algorithm –
With over 2 billion active users, Facebook is the largest social media platform yet. To moderate content on the platform, Facebook employs several algorithms to determine what content users will find interesting. The updated algorithm considers:
Timing:
Facebook’s algorithm prioritizes timing as a key signal for ranking content in users’ feeds.
Demographics:
The user’s location, language, and gender can help predict their content preferences.
Account credibility:
The algorithm ranks accounts with credibility, a strong following, and engaging content.
Content type:
Facebook’s algorithm prioritizes the type of posts users engage with most. So, users who watch more videos will get more video content.
Relevance:
Posts with similar keywords or hashtags are recommended to users with shared interests.
Engagement levels:
High engagement, including comments, shares, and likes, indicates quality content, and since Facebook is all about creating meaningful posts, it ranks it higher.
Facebook connections:
Content from the accounts you follow is prioritized over those you don’t follow.
Facebook’s algorithm combines these signals to give the post a relevancy score, which predicts how likely a user is to interact with a post.
Instagram algorithm –
Instagram’s algorithm is one of the most diverse social media platforms. According to Instagram CEO Adam Mosseri, Instagram is divided into 5 sections: Feed, Stories, Explore, Reels, and Search. Each section uses algorithms, classifiers, and processes to tailor content recommendations based on user behavior.
Instagram Feed
Your Instagram feed is a mix of content from accounts you follow, similar profiles you’re likely to enjoy, and personalized ads. In the feed, posts are ranked based on several signals:
Post recency:
Recent posts from accounts take priority, appearing before older ones.
People you follow:
Posts from those you follow appear organically on your feed. For creators, this means that having more followers increases your visibility.
User activity:
Posts you’ve liked, shared, saved, or commented on convey your content preferences.
Content type:
Users who prefer photos will see more photos. The same goes for videos and carousels.
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Post information:
Posts with many likes, shares, comments, and saves signal relevance to users with similar interests or locations.
Interaction history:
Your interactions with an account’s posts and frequency influence the appearance of their content in your feed.
Instagram Stories
Stories is an in-app feature that allows users to post photos or videos that disappear after 24 hours. Here’s what it looks like:
The ranking factors for stories are quite similar to those of the IG Feed. Stories use engagement signals such as:
Viewing history:
Frequently viewing an account’s stories would result in its prioritization.
Engagement history:
Liking or responding to stories is an engagement metric that impacts story rankings.
Closeness:
The algorithm considers your relationship with the story’s creator, including mutual follows, shared locations, and DM interactions.
Instagram Explore
Explore simplifies the discovery of new posts without needing active search. Here’s what it looks like on Instagram:
The grid comprises content recommendations from accounts you don’t follow but Instagram believes you might enjoy. Signals Instagram considers include:
User’s past interaction:
Content is ranked based on previous engagements with posts.
Post popularity:
The number of likes, comments, shares, and saves influences visibility.
Users’ explore activity:
History of a user’s interaction on the Explore page, including liked posts and similar content.
Account information:
The frequency of interaction with the account in the past few weeks.
Instagram Reels
Reels are designed for users to explore new content from accounts they don’t follow, similar to Explore. Key signals for Reels content include:
User’s activity:
Recent interactions with Reels, such as likes, saves, shares, comments, and engagement.
Interaction history:
Your interaction history with accounts, even if you don’t follow them.
Relevance:
The content’s relevance is determined by popularity signals like likes, saves, and comments.
Account information:
An account’s popularity, including follower count and engagement level, informs content recommendations.
YouTube algorithm –
YouTube’s algorithm considers many signals to rank videos on its homepage, including:
Video performance:
YouTube video performance is measured in terms of engagement metrics such as likes, shares, and comments.
Click-through rate:
YouTube’s algorithm assesses the likelihood of user interaction with a video to recommend it to other users.
Watch time and retention:
Videos with longer watch time indicate quality content that retains viewer attention.
Recency:
Newer videos are promoted to subscribers who interact with the channel.
User watch history and actions:
Video watch time and post-interaction actions, like liking and commenting, signal relevance to the algorithm.
Search history:
What has the user searched for? Is it a recurring search?
Demographics and location:
Videos uploaded by creators in a particular location are pushed to local viewers before broader promotion.
LinkedIn algorithm –
LinkedIn’s algorithm uses several ranking signals that contribute to the success of a post on the platform. Here are some to watch out for:
Quality over quantity:
LinkedIn’s algorithm filters posts into three categories: spam, low-quality, and high-quality.
Relevance:
The algorithm assesses post relevance based on keywords, hashtags, and comments.
Engagement probability:
LinkedIn uses machine learning models to predict post engagement, especially within the first hour.
Personal connections:
LinkedIn’s algorithm prioritizes posts from your 1st-degree connections and those you engage with.
Consistency:
The algorithm rewards pages that post regularly with increased visibility.
Credibility:
Author expertise, insightful content, and meaningful comments enhance post ranking.
Recency:
Recent posts hold significance for LinkedIn’s algorithm.
Content type:
The algorithm loves short-form videos and well-structured long-form content.
AI’s role in social media algorithms
The fact is, without AI, social media wouldn’t exist in the first place. AI has played a significant role in developing and curating algorithms to learn about users and make deliberate decisions based on the collected data. Here are some ways AI plays a role in social media algorithms:
1. Flagging of misinformation and fake news –
Social media platforms use AI to detect fake news and filter out offensive, harmful, and disrespectful content. For instance, Twitter uses machine-learning models, AI-driven algorithms, user reporting, and human moderation teams to identify and flag misinformation. The algorithm analyzes the tweet content and account history to fact-check the accuracy of the information shared.
2. Moderate content for user safety –
It’s fair to say that social media can be unpleasant due to trolls who engage in offensive behavior, such as making hateful comments and, in worse situations, harassment. Ex-Facebook uses an AI tool to detect abuse and fraud in posts, images, and videos, with human reviewers stepping in when needed.
3. Personalize content delivery –
AI-driven algorithms segment users based on explicit (follows and likes) and implicit (video-watching time) details to personalize content recommendations. Users can select preferred keywords, follow hashtags, or hide inappropriate words in their Feeds and DMs.
4. Provide real-time analytics –
AI algorithms can collect, process, and analyze data as soon as it is generated. Platforms like Facebook, which record billions of daily users, use generative AI for rapid predictions, understanding user relationships, and addressing security issues in real time.
FAQ(Frequently Asked Questions)
How do I control my social media algorithm?
Control your social media algorithm by engaging with content you love, unfollowing irrelevant accounts, using search intentionally, saving valuable posts, and consistently interacting to train platforms around your true interests.
What was the first social media algorithm?
The first social media algorithm emerged with Facebook’s 2006 News Feed, prioritizing posts based on relevance and engagement rather than time, forever changing how users discovered content on digital platforms.
How do social media algorithms affect mental health?
Social media algorithms can impact mental health by promoting addictive scrolling, reinforcing negative content loops, increasing comparison, reducing self-esteem, and creating echo chambers that intensify anxiety, loneliness, and emotional stress.
Why social media algorithms are good?
Social media algorithms are good because they personalize content, enhance user experience, connect like-minded communities, boost the discovery of relevant information, save time, and help businesses reach their ideal audiences effectively.
ABOUT THE AUTHOR –
DEEPAK KUMAWAT
Passionate about digital marketing, I specialize in SEO, PPC, social media, and content strategies, helping businesses grow online with innovative solutions, proven expertise, and a results-driven approach.