随着Web 2.0 时代网络技术的快速发展,社交类网站用户大规模增加。该文选取腾讯微博近两万名用户,抓取了他们所有的微博数据,对腾讯微博的用户内容生成模式进行分析和研究。我们从微博用户贡献分析、基于时间的用户活跃度分析以及微博影响三个角度出发,对微博的数量、微博的原创与转发、微博发布的周模式与日模式、微博转发影响力以及对影响微博转发的因素进行研究。总结出微博用户内容生成的一些特点,如用户内容贡献呈现一种“90-10”规则,不同类型的用户有着不同的“微博风格”,微博用户每日微博发布数有着明显的周模式与日模式等。相关分析结论对于进一步深化研究微博的用户内容生成模式具有一定参考意义。
Abstract
With the rapid development of network technology in Web2.0 age, the amount of social network website users has increased sharply. This paper colllects near 20 thousands users of Tencent Microblogging with their Microbloggings, and analyzes the patterns of user Content Generation of Tencent Microblogging. From perspectives of Microblogging content contribution, user activity over time and Microblogging influence, we examine the amount of Microblogging, ratio of original and repost content, number of content text, the weekly and daily patterns of Microblogging release, the repost number of Microblogging, the repost influence of Microblogging and the Microblogging contain ‘@’. Our analysis shows observations scuh as the users content contribution have “90-10”rule, different type of users have different “Microblogging style”, and users’ posting behavior exhibits strong daily and weekly patterns.
关键词
微博 /
用户内容生成 /
模式分析
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Key words
Microblogging /
User Content Generation /
Pattern Analysis
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参考文献
[1] F Bell. The rise of user-generated content. http://www.entrepreneur.com/technology/managingtechnology/web20columnistfrankbell/article183432.html, 2007.
[2] Lei Guo, Enhua Tan, Songqing Chen, et al. Analyzing Patterns of User Content Generation in Online[C]//Social Networks KDD09, Paris, France, 2009: 495-503.
[3] Haewoon Kwak, Changhyun Lee, Hosung Park,et al. What is Twitter, a Social Network or a News Media? [C]//ACM, Raleigh, North Carolina, USA, 2010: 799-806.
[4] Meeyoung Cha,Hamed Haddadi, Fabr′icio Benevenuto, et al. Measuring User Influence in Twitter: The Million Follower Fallacy[J].Artificial Intelligence, 2010,146(1):10-17.
[5] Jianshu Weng, Ee-Peng Lim, Jing Jiang, et al. TwitterRank:Finding Topic-sensitive Influential Twitterers[C]//WSDM10,February4-6,New York City, New York,USA
[6] 王晓兰. 2010年中国微博客研究综述[J].国际新闻界,2011, 24(1):24-27.
[7] 余伟. 基于本体的微博客用户行为模型研究[J].广东技术师范学院学报,2010,27(2):27-30.
[8] 高承实,荣星,陈越. 微博舆情监测指标体系研究[J].情报杂志,2011,30 (9):66-70.
[9] 谢丽星,周明,孙茂松. 基于层次结构的多策略中文微博情感分析和特征抽取[J].中文信息学报,2012,26(1):73-82.
[10] 杨亮,林原,林鸿飞. 基于情感分布的微博热点事件发现[J].中文信息学报,2012,26(1):84-90.
[11] 2012中国微博微博蓝皮书[R].DCCI互联网数据中心,2012年9月.
[12] 李燕萍. 微博比较研究[J].情报杂志,2012,31(3):77-83.[13] Zi Yang, Jingyi Guo, Keke Cai, et al. Understanding Retweeting Behaviors in Social Networks[C]//CIKM10,October 26-30, Toronto, Ontario, Canada.
[14] Danah boyd, Scott Golder, Gilad Lotan, Tweet,Tweet,Retweet: Conversational Aspects of Retweeting on Twitter[C]//Proceeding of the 43rd Hawaii International Conference on System Sciences, 2010.
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脚注
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基金
国家自然科学基金(70871001,71271211);北京市自然科学基金(4132067);中国人民大学科学研究基金(中央高校基本科研业务费专项资金)
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