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| In recent years, social network users have been the focus of research, but existing research often divides users into normal and malicious users, lacking a more detailed analysis. To address the absence of a precise user classification model, this paper builds a three-dimensional classification model including Anti-bot Analysis, Publicity, and Hashtag Manipulation for measuring user behaviors and classifying users into four categories: harmless users, publicizing users, hashtag hijacking users, and malicious publicizing users. Based on the classification model, this paper has built a set of user classification features, and introduces four new features. We also create two new Weibo user datasets with new features, and re-processes an existing Weibo user dataset. The experimental results show that the model and features proposed in this paper have good classification efficiency for precise Weibo user classification and bot behavior recognition, and have obvious classification improvement for decision trees and BP neural networks. |
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| Keywords:Computer Technology, Weibo, User Classification, feature |
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