Mechanisms of Algorithmic Recommendation on Social Media Opinion Polarization

With the rise of short video platforms, algorithmic recommendation systems, while enabling personalized content distribution, have been criticized for fueling social media opinion polarization. This study empirically explores how algorithms drive such polarization on mainstream short video platforms. Integrating user behavior, content distribution patterns, and interactive networks, it examines algorithms' filtering and amplification in information flow. Through a theoretical model and multi-source data, it reveals how algorithms shape viewpoint exposure and extreme opinion spread, identifying varying polarization impacts across algorithms and platforms. Via flowcharts and data models, it elaborates on coupling mechanisms among content push, interaction, and differentiation. Finally, targeted algorithm optimization suggestions reduce polarization risks, aiding governance and healthy online ecosystems.