An accurate identification method of abnormal users in social network based on multivariate characteristics

Abstract

In this paper, an accurate identification method of abnormal users in social networks based on multiple features was proposed. Firstly, the API interfaces provided by social networks are used to capture social network user data, so as to extract multiple features such as account features, text features and behaviour characteristics of users. Then, attribute reduction method is used to remove redundant features and obtain accurate user attribute feature set. Finally, based on the user attribute feature set, XGBoost model was used to construct the objective function of accurate identification of abnormal users in social networks, and the accurate identification results of abnormal users in social networks were obtained. Experimental results show that the feature extraction accuracy of abnormal users in social networks by the proposed method is more than 95%, the identification error rate varies between -6.3% and 3.6%, and the identification time is less than 0.6 s.