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An Empirical Feasibility Study of Societal Risk Classification Toward BBS Posts

  • Jindong Chen
  • Xiaoji Zhou
  • Xijin Tang
Article
  • 32 Downloads

Abstract

Societal risk classification is the fundamental issue for online societal risk monitoring. To show the challenge and feasibility of societal risk classification toward BBS posts, an empirical analysis is implemented in this paper. Through effectiveness analysis, Support Vector Machine based on Bag-Of-Words (BOW-SVM) is adopted for challenge validation, and the distributed document embeddings of BBS posts generated by Paragraph Vector are applied to feasibility study. Based on BOW-SVM, cross-validations of BBS posts labeled by different groups and annotators are conducted. The big fluctuation of cross-validation results indicates the differences of individual risk perceptions, which brings more challenges to societal risk classification. Furthermore, based on the distributed document embeddings of BBS posts, the pairwise similarities of more than 300 thousands BBS posts from different societal risk categories are compared. The higher similarities of BBS posts in the same societal risk category reveal that BBS posts in the same societal risk category share more features than BBS posts in different categories, which manifests the feasibility of societal risk classification of BBS posts, and also reflects the possibility to improve the performance of societal risk monitoring.

Keywords

Societal risk classification Tianya Forum cross validation pairwise similarity individual risk perception 

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Notes

Acknowledgments

This study is supported by National Natural Science Foundation of China under grant Nos. 71601023, 61473284, 71731002 and L1624049, the Supplementary and Supportive Project for Teachers at Beijing Information Science and Technology University (2018–2020) (5029011103) and National Key R&D Program of China (2017YFB1400500).

The authors would like to thank other members of our group for their effort in data collection and post labeling.

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Copyright information

© Systems Engineering Society of China and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Economics and ManagementBeijing Information Science & Technology UniversityBeijingChina
  2. 2.China Academy of Aerospace Systems Science and EngineeringBeijingChina
  3. 3.Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijingChina
  4. 4.Beijing Key Lab of Green Development Decision Based on Big DataBeijingChina
  5. 5.University of Chinese Academy of SciencesBeijingChina

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