
How Social Media Recommendation Algorithms Shape Content, Behavior, and Regulation Worldwide
Social media platforms such as TikTok, Instagram, and YouTube rely on sophisticated recommendation systems that analyze user behavior to predict engagement rather than simply ordering posts chronologically. These algorithms form candidate sets and rank content using signals including watch time, likes, comments, and device data, creating personalized feeds that can diverge significantly between users. Research shows mixed effects on political views and mental health, with personalization capable of both amplifying polarization and reducing it depending on optimization goals. Regulatory responses vary: China mandates transparency and youth mode limits on Douyin, the EU's Digital Services Act requires non-profiled alternatives for large platforms, and Russia enforces disclosure rules under Federal Law No. 408-FZ since 2023. Practical steps allow users to reset recommendations and provide negative feedback to retrain feeds. The article debunks myths of dopamine addiction and inevitable radicalization while highlighting both risks and benefits of algorithmic curation.
SecuritylabSep 24, 2026Translated from Russian

