An overview of opinion polarization: models, drivers, and strategic solutions

In the context of social media and algorithmic personalization shaping information flows, opinion polarization poses a significant challenge to information processing and decision making. In this paper, we retrieved Web of Science records and applied a multi-stage screening process, yielding 145 rigorously selected papers on opinion polarization. From these sources, we developed a three-dimensional classification framework categorizing polarization models into individual behavior, group dynamics, and network structure. Our analysis reveals that cognitive bias in information processing, identity homophily within social groups, and algorithmic filtering in online platforms serve as core drivers of digital opinion polarization. Based on these insights, we propose strategic solutions across four interdependent domains: information level interventions, dialogue level facilitation, technology level adjustments, and psychology level guidance. We illustrate how these measures can be operationalized to mitigate echo chamber effects and foster cross-cutting engagement. This review synthesizes existing research to offer a structured foundation for understanding complex polarization mechanisms. It also provides a theoretical basis for future cross-platform dynamic modeling and policy development. Finally, we identify critical research gaps and outline future directions, highlighting the need for adaptive AI-driven moderation strategies, dynamic polarization models, and coordinated cross-platform policy interventions in the evolving digital landscape.