Evaluating the effectiveness of the “Double Reduction” education policy in China: A study using web scraping, sentiment analysis, and spatial regression - Xiao, Yan; Xie, Jinchen (PLOS ONE, 2025)
Background The implementation of China’s “Double Reduction” (DR) policy, which aims to alleviate academic and extracurricular burdens, has received considerable attention. However, there has been limited evaluation of public satisfaction with the policy, particularly from a regional and multi-dimensional support perspective. This study aims to assess DR policy satisfaction from Chinese public, through a comprehensive “government–market–school” perspective. Methods Combining the web scraping technology and sentiment analysis technology, this study captures 2,475,833 Weibo posts from 31 provinces in China related to DR policy. The causal relationship is discussed through spatial regression after controlling for spatial endogeneity. Results The findings indicate that Chinese residents generally express positive satisfaction with the DR policy, however, substantial regional disparities persist. Provinces in the western and central regions exhibit lower increases in DR policy satisfaction (DRS) compared to those in the eastern region. All three dimensions—political, market, and educational support—have significant positive effects on DRS. Moreover, the results reveal positive moderations among the three types of support. Political support exerts a stronger influence on DRS in western provinces, whereas market support plays a more prominent role in eastern provinces. No significant interprovincial variation is observed for the effects of educational support. Conclusions The study highlights the crucial role of political, market, and educational support in shaping public satisfaction with the DR policy. These findings suggest that targeted interventions are needed to address regional disparities, particularly in underdeveloped areas. Future research should focus on the long-term effects of the DR policy across diverse socio-economic contexts.