基于隐空间代价敏感学习的微博水军识别方法  

Microblog Spammer Identification Method Based on Cost-sensitive Learning in Latent Space

作　　者：王磊[1] 任航 王之怡[1] WANG Lei;REN Hang;WANG Zhiyi(School of Economic Information Engineering,Southwestern University of Finance and Economics,Chengdu 610074,China)

机构地区：[1]西南财经大学经济信息工程学院,成都610074

出　　处：《计算机工程》2018年第9期159-163,170,共6页Computer Engineering

基　　金：中央高校基本科研业务费重大理论基础研究项目(JBK151127);中央高校基本科研业务费创新团队项目(JBK130503;JBK150503);教育部人文社会科学研究西部和边疆地区项目(16XJAZH002)

摘　　要：根据微博水军活动的特点,提出一种基于隐空间代价敏感学习的半监督水军识别方法。从内容、行为、社交关系3个视角选取微博账户的22个特征,结合矩阵隐空间分解、代价敏感学习和社交关系正则技术,构造代价敏感的半监督最大间隔分类模型,并利用随机梯度下降算法求解模型的线性复杂度。实验结果表明,该方法在准确率、召回率和F1指标上均优于SMFSR和L2-SVMs方法,并且具有接近线性的学习速度。According to the characteristics of microblog spammers,this paper proposes a semi-supervised spammer identification method based on cost-sensitive learning in latent space.Firstly,it selects twenty-two features of microblog account from perspectives of contents,activities and social relations.Then,it obtains latent account vectors using matrix factorization method and constructs a novel cost-sensitive semi-supervised classification model with the maximum margin theory in latent space.In addition,a social relation regularization from following behaviors is formulated on the model.Finally,it develops a linear-complexity algorithm for solving the model with the stochastic gradient descent method.Experimental results show that the proposed method outperforms existing methods significantly,such as SMFSR and L2-SVMS,in terms of the evaluation measures of accuracy,recall and F1 score.It also obtains nearly linear training speeds.

关 键 词：水军识别 矩阵分解 代价敏感学习 社交关系正则 隐空间 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]