新浪微博隐式组织发现  

Detecting Implicit Organization on Sina Weibo

作　　者：刘程[1] 沙灜 姜波[1] 郭莉[1] 

机构地区：[1]中国科学院信息工程研究所,北京100093

出　　处：《中文信息学报》2017年第2期139-145,219,共8页Journal of Chinese Information Processing

基　　金：中国科学院院战略先导专项(XDA06030200);国家科技支撑计划(2012BAH46B03);国家自然科学基金(61272427)

摘　　要：社交网络中往往同时存在多种类型的账号,如正常个体用户、水军、僵尸粉、蓝V组织等。我们把其行为呈现为组织特性的个体账号,定义为隐式组织。隐式组织通常背后有相应的组织团队负责账号的运营,因此其行为模式呈现为组织的行为模式,有别于个体账号。隐式组织的有效发现对于社交网络中舆情传播趋势分析、广告推荐等都有重要的意义。该文以新浪微博数据为例,在数据采集系统基础上,共人工标注了583个账号,提取了22个特征,使用朴素贝叶斯和决策树算法,实现了对隐式组织的有效识别,其准确率达86.4%,并分析得出了特征的重要程度排序。实验证明了社交网络中存在隐式组织,其行为特征是可以识别的。Various types of account tend to be existed in Social network, including normal individual users, online water army, zombie fans, official organizations and so on. We define the individual accounts whose behavior is rendered as organizational characteristic as impli-cit organization. With a team responsible for the operations, the implicit organization account bears no individuals＂ behavior pattern, but falls in the pattern of an official organization. The effective discovery of implicit organizations have important significance for analysis of public opinion trends in the spread of social networks, advertising recommendations and so on. This paper, taking the data of SinaWeibo as an example, investigates the classification of the individuals and the implicit organizations. We manually labeled a total of 583 accounts, and summarizing 22 related features to build a Naive Bayes model and a decision tree model. Experiments demonstrate an effective identification of implicit organization by 86.4% precision.

关 键 词：社交网络 隐式组织 机器学习算法 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]