基于行为特征分析的微博恶意用户识别

近年来,社交网络数据挖掘作为物理网络空间数据挖掘的一大热点,目前在用户行为分析、兴趣识别、产品推荐等方面都取得了令人可喜的成果。随着社交网络商业契机的到来,出现了很多恶意用户及恶意行为,给数据挖掘的效果产生了极大的影响。基于此,提出基于用户行为特征分析的恶意用户识别方法,该方法引入主成分分析方法对微博网络用户行为数据进行挖掘,对各维度特征的权重进行排序,选取前六维主成分特征可以有效识别恶意用户,主成分特征之间拟合出的新特征也能提升系统的识别性能。实验结果表明,引入的方法对微博用户特征进行了有效的排序,很好地识别出了微博社交网络中的恶意用户,为其他方向的社交网络数据挖掘提供了良好的数据清洗技术。

Microblogging Malicious User Identification Based on Behavior Characteristic Analysis

In recent years,as a hotspot in data mining of physical network,social network data mining has made grati-fying achievements in the current user behavior analysis,interest recognition and product recommendation.With the advent of social networking business opportunities,many malicious users and malicious behaviors have also emerged,which have a great impact on the effectiveness of data mining.A malicious user identification method based on user behavior feature analysis was proposed.This method uses the principal component analysis(PCA) to mine the user behavior data in microblogging network,and ranks the weight of each feature.It can effectively identify malicious users with first six-dimensional principal component features.The new features fitted by the principal component features are used to improve the recognition performance of the system.The experimental results show that the proposed method can effectively sort the microblogging user features and identify the malicious users in the microblogging social network,which provides a good data cleaning technique for social network data mining in other directions.