The Challenges and Feasibility of Societal Risk Classification Based on Deep Learning of Representations

Using the posts of Tianya Forum as the data source and adopting the socio psychology study results on societal risks perception, we analyze the challenges and feasibility of the document-level multiple societal risk classification of BBS posts. To effectively capture the semantics and word order of documents, a deep learning model as Post Vector is applied to realize the distributed vector representations of the posts in the vector space. Based on the distributed vector representations, cross-validated classification of the posts labeled by different annotators with KNN method and pair wise similarities comparisons of the posts between risk categories are implemented. The big variance of the results of cross validation shows the differences of individual risk perceptions, which reflects the challenges of societal risk classification. Furthermore, the higher similarities of posts in same societal risk category manifest the feasibility of the classification of societal risks, and indicate the possibility to improve the performance of the societal risk classification of BBS posts.