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How TikTok, Instagram, and X (Twitter) recommendation algorithms work

Social networks rely on sophisticated recommendation algorithms to grab and hold users' attention by offering content tailored to their preferences and behaviors. Their main goal is, of course, to maximize the platforms' profits (As a shareholder, that works out nicely for me ^^). Let's go over the main criteria of these algorithms, or at least what we know about what they consider—because transparency isn’t exactly their strong suit.

TikTok: The power of endless watching

TikTok is probably the most iconic platform when it comes to personalization. Its algorithm is based on the Monolith system (unofficial name, likely a bundle of machine learning recommendation models), which can analyze each interaction in real time:

  • Watch time and replays: a strong signal of genuine interest in a video.
  • Interactions: likes, shares, comments directly influence visibility.
  • Trends: videos tied to viral music or hashtags get an algorithmic boost.

Unlike other networks, follower count plays a secondary role. The "For You" feed is built mainly on the user’s personal behavior and the video's ability to capture attention (though it also considers signals like global popularity, regional trends, etc.).

As I'm no spring chicken, I don’t really know this platform—I’m an old guy who sticks to Facebook ._.'

Instagram: Multiple algorithms for different formats

Instagram doesn’t have a single algorithm, but different systems for each of its spaces (Feed, Stories, Reels, Explore). Adam Mosseri, the CEO, emphasizes this diversity:

  • Reels and explore: favor discovering new creators via global interaction analysis.
  • Feed and stories: prioritize existing relationships (friends, family, followed accounts).
  • AI and machine learning: every action (like, comment, watch time) enriches the user’s history.

Creators who succeed on Instagram are those who post regularly, optimize content distribution by considering contextual signals (posting time, content type, device used, etc.), and encourage "authentic" interactions with their audience, meaning meaningful likes, comments, and shares rather than superficial ones.

X (Twitter): The importance of engagement and recency

X has significantly revamped its algorithm in recent years (Elon isn’t exactly known for playing it safe haha). The feed now mixes content from follows and recommendations filtered through:

  • Engagement: tweets with likes, retweets, replies, or clicks rise in visibility.
  • Recency: the platform strongly favors recent content to promote real-time relevance.
  • Credibility and user behavior: perceived account quality (age, consistency, past interactions) affects reach.
  • Rich media: tweets with images, videos, or polls get boosted.

However, X is now under regulatory watch (notably by the European Union via the DSA) to verify transparency and ideological biases in its recommendation system. From my very subjective point of view, algorithms may diverge by continent in the coming years. And while the EU isn’t famous for speed, I doubt it will stay idle long with foreign election meddling...

Conclusion

Although their approaches differ, TikTok, Instagram, and X share the same goal: maximize time spent and engagement. TikTok relies on instant watch analysis, Instagram on multiple models adapted to formats, and X on rapid engagement and recency. For creators and brands, understanding these logics is essential to adapt content and maximize visibility.

Some of my statements might be a bit too categorical—remember that this remains very opaque, and the algorithms aren’t fully publicly known.

These algorithms evolve constantly to optimize platform profits. It’s crucial to stay informed about updates to fully leverage them and avoid being outpaced by competitors!

cyrilou