Social media algorithms aren't mysterious entities but mathematical systems optimizing for engagement. They use collaborative filtering and content-based filtering to analyze user behavior, tracking clicks, watch time, and searches to predict what content will keep users scrolling longer on platforms.

The implementation relies on cosine similarity to find users with similar behavior patterns. Microsoft's MIND dataset demonstrates this approach, where the algorithm identifies "nearest neighbors" and recommends content they've engaged with using weighted averages across 50,000 users and 51,000 articles.

These systems create filter bubbles by continuously serving content matching existing preferences. A sports fan's initial curiosity about politics quickly leads to an algorithmically-curated feed dominated by sports content (40%), demonstrating how recommendation systems reinforce existing interests while limiting exposure to diverse perspectives through mathematical optimization rather than conscious design.