Analysis on the Policy Performance of Public Support on Higher Education: Applying Social Big Data based Analysis on University Specialization Projects

Background/Objectives: As the working environment for higher education institutions is worsened, in-depth analyses are needed to look at the relationship between educational performance and public support programs, university specialization projects in Korea. Methods/Statistical analysis: This study uses information from social network services such as tweeter. Data is obtained from keyword search on university specialization projects in tweeter and analyzed by informetric analysis and then organized categorically using NodeXL. The analysis method relies on frequency analysis and text network analysis to find the relationship between languages expressed in the text and the connection network of concepts. Findings: Three analyses are conducted on data from social network analysis. Center content analysis identifies several keywords that pay special attention to universities in rural regions, separating them from ones in the capital region. Annual change analysis takes out keywords such as locality and specialty, which represents the core idea of the university specialization projects. Betweenness centrality analysis finds the terms like local university development and specialization as core concepts, showing the trusty signs of the program. These analyses, in turn, show the mass evaluation on the program as a whole. Improvements/Applications: This study, showing the weakness on getting more data and information, needs to extend the scope of search for related words and concepts in analyzing the government support on higher educational institutions. Systematic approaches are demanded upon getting extended amount of data and laying out the related concepts.