Economic policies assessment and judgement during the pandemic with semantic and social network joint analysis

Abstract

This paper delves into the economic policies of China during the pandemic and investigates the relationships between policy-issuing institutions. Firstly, we conduct keyword extraction and statistical analysis based on policy texts to understand policy contents and distribution. Then, we calculate the co-occurrence matrix of policy keywords and publishers from social networks and visualise the relationships with UCINET6 and Gephi software. Through a combination of semantic analysis and social network analysis, we examine the content of relevant economic policies, laws, regulations, and the relationships between their publishers. Our findings reveal three stages of China’s economic policies during the crisis, i.e. the shock, stable, and boost periods, which align with the crisis’s impact on China’s economy. Initial policies supported small-sized enterprises (SMEs), followed by a focus on industries like tourism. During the boost phase, policies underscored various support measures, including tax and fee reductions. We also identified a “local agglomeration” characteristic among policy-issuing entities, suggesting potential improvements in cooperation, especially at the provincial level. Our findings provide valuable insights for future policy design in response to public security events.