关键词: 数据挖掘, 微博, 炒作群体, 最大频繁项集

Abstract: In recent years, the hype accounts in Microblogs rise as a new force, using illegal means to carry out the network public relations activities, which has seriously disturbed the normal order of the Internet. The traditional detection of hype accounts mainly uses methods based on feature analysis, ignoring that hype accounts are strongly organizational and planning, which is difficult to find the concealed ones. In view of the above problems, fully considering the group characteristics that hype accounts often participate in hype microblogs together, the problem of hype groups detection is transformed into the problem of mining maximum frequent itemsets, and a method based on mining maximum frequent itemsets for the detection of hype groups is proposed, which can find accounts groups who have participated in hype microblogs together in many times. According to the research background and the characteristics of transaction database, a new algorithm based on iterative intersection is proposed to improve the efficiency of mining maximum frequent itemsets, which uses a selection strategy based on binary search algorithm to reduce the transaction database, and uses a variety of ways to reduce the times of intersection between transactions. Finally, the performance of IIA algorithm is evaluated by experiments, and experiments are conducted on a real dataset from Sina Weibo, the experiments results show that this method can find highly concealed hype accounts that can’t be identified by traditional methods based on feature analysis, with the accuracy rate of up to 90%.

Key words: data mining, microblog, hype groups, maximum frequent itemsets