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Twitter的Follow关系和Retweet关系对比 被引量:1

Comparison between Follow and Retweet on Twitter
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摘要 研究Twitter在线社交网络中,Follow关系和Retweet关系在传播用户影响力和表征用户同质性这两方面的差异。为研究两者在传播用户影响力上的差异,定义了V f变量和V r变量分别度量Follow关系和Retweet关系的作用;为研究两者在表征用户同质性上的差异,分别基于Follow关系和Retweet关系构造出对应的社交网络图,并采用wvRN算法分别对两个网络内的用户进行分类。通过对比用户的V f变量值和V r变量值发现,Retweet关系在传播用户影响力方面的作用优于Follow关系;通过对比分类结果发现,Follow关系比Retweet关系更能表征用户的同质性,基于Follow关系的分类精度比基于Retweet关系的分类精度高20%,分类结果同时揭示不同类别的用户体现出了不同的关注和信息互动特性。基于上述研究说明Follow关系和Retweet关系所携带的信息是不同的。 This work aimed to compare the differences between Follow relationship and Retweet relationship in propagating the Twitter users influence and measuring the Twitter users homophily. In order to find their differences in the first aspect, this paper defined two verities: Vf was defined for the Follow relationships and Vr was for the Retweet relationships. In order to analyze the differences in measuring the homophily, it built two social network separately based on Follow relationships and Retweet relationships. Then it adopted the wvRN algorithm to classify users in these two networks. By comparing the Vf and Vr values for a group of user, it can be found that the Retweet relationship win in propagating the users influence. By comparing the classification results, it can be found that the Follow relationship contains more homophily information, that the accuracy based on Retweet is 20% higher than that based on Retweet. But the results also illustrates that different kinds of users have different communication characteristics. This research shows that Follow relationships and Retweet relationships contain different information.
作者 曾雪 吴跃
出处 《计算机应用研究》 CSCD 北大核心 2014年第1期192-195,共4页 Application Research of Computers
关键词 在线社交网络 网络数据分类 同质性 推特网 online social network networked data classification homophily Twitter
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参考文献10

  • 1MICHELSON M,MASCKASSY S A. Discovering users' topics of interest on Twitter:a first look[A].2010.73-79.
  • 2MASCKASSY S A,MICHELSON M. Why do people retweet? Antihomophily wins the day[A].California:The AAAI Press,2011.209-216.
  • 3KANG J H,LERMAN K. Using lists to measuring homophily on twitter[A].California:The AAAI Press,2012.26-32.
  • 4LEE K,PALSETIA D,NARAYANAN R. Twitter trending topic classification[A].2011.251-258.
  • 5KOMAMIZU T,YAMAGUCHI Y,AMAGASA T. FACTUS:faceted Twitter user search using Twitter lists[A].Heidelberg:Springer-Verlag,2011.343-344.
  • 6HU Meng-die,LIU Shi-xia,WEIFu-ru. Breaking news on Twitter[A].2012.2751-2754.
  • 7ZENG Xue,WU Yue,ZHENG Li-hua. Homophily modeling for a single class inrelational classification[J].Advances in Information Sciences and Service Sciences,2012,(04):667-674.
  • 8MACSKASSY S A,PROVOST F. Classification in networked data:a toolkit and a univariate case study[J].JOURNAL OF MACHINE LEARNING RESEARCH,2007,(05):935-983.
  • 9MCPHERSON M,LOVIN L S,COOK J M. Birds of a feather:homophily in social networks[J].Annual Reviews of Sociology,2011,(01):415-444.
  • 10WELCH Michael J,SCHONFELD Uri,HE Dan,CHO Junghoo. Topical semantics of Twitter links[A].2011.327-336.

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  • 1Kahanda I, Neville J. Using transactional information to predict link strength in online social networks[C]//Procee- dings of the 3rd International Conference on Weblogs and Social Media, San Jose, USA, May 17-20, 2009. MenloPark, CA, USA: AAAI, 2009: 74-81.
  • 2Xiang Rongjing, Neville J, Rogati M. Modeling relation- ship strength in online social networks[C]//Proceedings of the 19th International Conference on World Wide Web, Raleigh, USA, Apr 26-30, 2010. New York, NY, USA: ACM, 2010: 981-990.
  • 3Arnaboldi V, Conti M, Passarella A. Dynamics of personal social relationships in online social networks: a study on Twitter[C]//Proceedings of the 1st ACM Conference on Online Social Networks, Boston, USA, Oct 7-8, 2013. New York, NY, USA: ACM, 2013: 15-26.
  • 4Cheng Zhiyuan, Caverlee J, Lee K. You are where you tweet: a content-based approach to geo-locating Twitter users[C]// Proceedings of the 19th ACM Conference on Information and Knowledge Management, Toronto, Canada, Oct 26-30, 2010. New York, NY, USA: ACM, 2010: 759-768.
  • 5Li Rui, Wang Shengjie, Chang K C C. Multiple location profiling for users and relationships from social network and content[J]. Proceedings of the VLDB Endowment, 2012, 5(11): 1603-1614.
  • 6Li Rui, Wang Shengjie, Deng Hongbo. Towards social user profiling: unified and discriminative influence model for inferring home locations[C]//Proeeedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Beijing, China, Aug 12-16, 2012. New York, NY, USA: ACM, 2012: 1023-1031.
  • 7Lee M J, Chung C W. A user similarity calculation based on the location for social network services[C]//LNCS 6587: Proceedings of the 16th International Conference on Database Systems for Advanced Applications, Hong Kong, China, Apr 22-25, 2011. Berlin, Heidelberg: Springer, 2011: 38-52.
  • 8Zheng Kai, Shang Shuo, Yuan N J, et al. Towards efficient search for activity trajectories[C]//Proceedings of the 29th IEEE International Conference on Data Engineering, Bris- bane, Australia, Apr 8-12, 2013. Piscataway, NJ, USA: IEEE, 2013: 230-241.
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  • 10Kim J, Lee E, Choi J. Monitoring social relationship among Twitter users by using NodeXL[C]//Proceedings of the 2013 International Conference on Reliable and Convergent Systems, Montreal, Canada, Oct 1-4, 2013. New York, NY, USA: ACM, 2013: 107-110.

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