Abstract: With the development of the Internet,Weibo has gradually become an important social media.However,in Weibo,abnormal users influence the behaviors of users by spreading harmful information,sending malicious links,and even launching malicious attacks,thus affecting the value of social networks.Therefore,it is important to realize the detection of abnormal users.Based on the Weibo abnormal users and normal user data sets obtained from multiple ways,this paper proposes to comprehensive extract and analyze various attributes of users.An abnormal user detection model is established through various data mining methods to identify abnormal user accounts.Experimental results of C4.5 decision tree and random forest algorithms show that by using the proposed method,the selected features are effective and the detection accuracy of abnormal users is high.

Key words: Abnormal user, Data mining, Feature extraction, Weibo