ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2015, Vol. 52 ›› Issue (2): 437-444.doi: 10.7544/issn1000-1239.2015.20140187

所属专题: 2015大数据管理

• 信息处理 • 上一篇    下一篇

基于微博的事件传播分析

朱湘,贾焰,聂原平,曲铭   

  1. (国防科学技术大学计算机学院 长沙 410073) (zhuxiang@nudt.edu.cn)
  • 出版日期: 2015-02-01
  • 基金资助: 
    基金项目:国家科技支撑计划基金项目(2012BAH38B04);国家“九七三”重点基础研究发展计划基金项目(2013CB329601);中国博士后科学基金项目(2013M542560);国家自然科学基金项目(61202362)

Event Propagation Analysis on Microblog

Zhu Xiang, Jia Yan, Nie Yuanping, Qu Ming   

  1. (College of Computer, National University of Defense Technology, Changsha 410073)
  • Online: 2015-02-01

摘要: 事件的传播分析是社交网络分析中一个重要的研究点.网络热点事件的爆发通过社交网络迅速传播,从而在短时间内造成很大的影响.而在社交网络中制造舆论热点进行传播的代价相对于传统媒介较低,因此很容易被不法分子利用,对社会安全以及人们财产造成损失.传统的影响传播分析仅能对单条博文进行影响传播分析,这使社交网络中的事件传播分析受到限制.在已有的独立级联模型的基础上,提出了一种结合用户去重、垃圾用户滤除和概率阅读的传播模型,其基本思想是对多条热点博文构成的事件进行用户去重,构建事件传播网络拓扑图,然后对其中的垃圾用户节点进行滤除,最后利用概率阅读模型进行影响传播分析.这为事件传播分析提供了思路.通过一系列实验来验证方法及模型,通过与传统的博文分析进行对比,验证了方法的正确性与有效性.

关键词: 社交网络, 传播模型, 影响力分析, 垃圾用户检测, 概率阅读模型

Abstract: Event propagation analysis is one of the main research issues in the field of social network analysis. Hotspot outbreaks and spreads through the social network, and it makes a great impact in a short period of time. Meanwhile, it is easier to create a hotspot and spread it in social network than in traditional media, so information diffusion will do harm to social security and property if used by criminals. Traditional influence propagation analysis method can only analyze single microblog (or tweet), so it limits event propagation analysis in social network. In this paper, we review some existing propagation models such as independent cascade model, linear threshold model, etc. After that, we introduce some basic definitions of influence propagation analysis in social network. Then we propose a method combining user deduplication, spammer detection and probabilistic reading based on existing independent cascade model. The main idea of our method is making user deduplication in the event composed of several key microblogs (or tweets) and building event propagation graph. Then we remove spammers in that graph and make influence propagation analysis by using probabilistic reading model. It provides a novel method to make event propagation analysis. Finally, some experiments are conducted and the results demonstrate the correctness and effectiveness of the method.

Key words: social network, propagation model, influence analysis, spammer detection, probabilistic reading model

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