微博信息采集及群体行为分析

发布时间：2018-06-13 20:08

本文选题：微博 + 信息采集　；参考：《小型微型计算机系统》2013年10期

【摘要】：随着在线社会关系网络的迅猛发展,每天数以千万计的人通过发表、评论、分享等方式,产生和传播各类话题.对在线社会关系数据的感知与收集、存储管理、群体行为等进行研究,能更好地挖掘和分析社会关系网络.由于微博平台的登录、数据显示与处理等方面与传统网络平台有很大差异,传统网络爬虫不适于对微博信息的全面抓取.本文采用模拟用户浏览行为方法来爬取海量微博数据,通过数据包截取与分析等手段获取相关信息.实验结果表明该方法的有效性.在此基础上,以收集的微博数据为研究对象,对群体行为进行了分析.
[Abstract]:With the rapid development of online social network, tens of millions of people each day through publishing, comments, sharing and other ways to produce and spread all kinds of topics. Research on the perception and collection, storage management, group behavior of online social relations data can better mining and analysis of social networks. Because of the login of Weibo platform, the data display and processing are very different from the traditional network platform, the traditional web crawler is not suitable for the Weibo information capture. In this paper, simulated user browsing behavior is used to crawl massive Weibo data, and relevant information is obtained by packet interception and analysis. The experimental results show that the method is effective. On this basis, the group behavior was analyzed based on the collected Weibo data.
【作者单位】：河北科技大学信息科学与工程学院;IBM
【基金】：河北省自然科学基金项目(F2013208105)资助河北省科技支撑计划项目(12213516D)资助
【分类号】：TP393.092;TP274.2

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