The study on the dissemination of waste sorting policies on social media and the public’s feedback attitudes: a text analysis based on comment data of policies in 46 key cities in China

This study investigates the dissemination of major waste sorting policies and public feedback attitudes across 46 key Chinese cities using data from the Weibo social media platform. The research employs a Latent Dirichlet Allocation (LDA) topic model to identify and mine themes from comment texts, extracting multiple core discussion topics. The results show that although negative sentiments slightly outweighed positive sentiments in public comments, there was no significant difference in the focal points of attention between positive and negative sentiments. Negative sentiments primarily centered on policy specifics and implementation methods, with key concerns including details of policy execution and operational challenges. Cities such as Shanghai, Beijing, Nanjing, and Hangzhou exhibited higher volumes of policy-related discussions, indicating greater public engagement in these regions. Analysis of IP address distribution revealed pronounced regional concentration, particularly among residents in developed eastern coastal areas. Finally, the study proposes strategic recommendations for optimizing information dissemination on social media to enhance public willingness to participate in waste sorting initiatives.