Public policy plays a critical role in coordinating social resources, providing public services, and promoting public value. However, few studies have systematically investigated the public's policy cognition from the objective aspect of policy content and the subjective aspect of target audience's policy support. To fill this gap, we explore the policy content via natural language processing and also policy support from the perspective of social psychology. Specifically, regarding the policy content analysis, we collect over one thousand policy documents and design an annotation system by analyzing the policy content, basic structure and text features, and then utilize text classification and information retrieval models based on the Chinese large-scale pre-trained models. Moreover, according to the policy tools identified in the content analysis, our paper investigates the audience's policy support for these tools and its influential factors. Based on a sample of 476 respondents from the whole country, we adopt the ordinary least square method to explore the key factors of policy support for supply, demand and environmental tools from three variables: political trust, policy effectiveness and knowledge of policies. This study contributes to existing literature by forming a structured understanding of policies and exploring public support for policy tools, which could benefit the practitioners during the policy-making process and also enhance successfully policy implementation.
Keywords: natural language processing; policy cognition; policy content; policy support; social psychology.
Copyright © 2022 Zhou, Dai, Ren, Chen and Chen.