Abstract:

Research on domestic social stability analysis mainly focuses on the construction of social stability theory, architecture and index, but little attention is paid to quantitative analysis. In this paper, we propose a social stability supervising framework based on sensitive Web information mining, semantic pattern matching and quantitative calculation. By analyzing the sensitive information about social environment, national harmony and happy index of people lives in natural language texts from Internet, and identifying hot keywords as well as the event trends led by the keywords, we construct a sensitive information knowledge base, and design a social stability index theoretic model and a quantitative calculation model to evaluate the social stability quantitatively. Parameters of the calculation model are determined by employing social investigations and an iterative feedback learning method. A prototype system is built on the proposed framework and experiments are conducted in 6 frontier provinces, such as Xinjiang and Tibet. The result of an average accuracy of 73.29% has reference value in decision-making to some extent. 

Key words: sensitive information;social stability index;web text mining