Data-Driven Social Security Event Prediction: Principles, Methods, and Trends

Social security event prediction can provide critical early warnings and support for public policies and crisis responses. The rapid development of communication networks has provided a massive data analysis base, including social media, economic data, and historical event records, for social security event prediction based on data-driven approaches. The advent of data-driven approaches has revolutionized the prediction of these events, offering new theoretical insights and practical applications. Aiming at offering a systematic review of current data-driven prediction methods used in social security, this paper delves into the progress of this research from three novel perspectives, prediction factors, technical methods, and interpretability, and then analyzes future development trends. This paper contributes key insights into how social security event prediction can be improved and hopefully offers a comprehensive analysis that goes beyond the existing literature.