政务微博影响力评价与比较实证研究——基于因子分析和聚类分析被引量：72

Positive Study on Evaluation and Comparison of Government Affairs Micro-blog Influence:Based on Factor Analysis and Cluster Analysis

下载PDF

导出

摘要以16家省级政务微博作为研究样本,甄选出4项一级指标、10项二级指标,构建政务微博影响力评价指标体系,并运用因子分析法和聚类分析法,进行政务微博影响力评价与比较实证研究。政务微博影响力绝大部分信息可以通过"公开-互动因子"和"获取-反馈因子"两个公因子反映出来。16家政务微博影响力水平可划分为5大类型,即强度领先型、综合中等型、综合落后型、均衡发展型和综合领先型。研究结果表明,政务微博影响力发展水平不均衡;低影响力政务微博偏多,高影响力政务微博相对较少,16家政务微博影响力呈底部巨大的"金字塔型"结构分布。 This paper takes 16 provincial government affairs micro-blogs as examples, establishes evaluation system composed of 10 inde-xes, and applies the method of factor analysis and cluster analysis to study influence evaluation and comparison of government affairs micro-blogs. Two components, namely“publicity-interaction factor” and“acquisition-feedback factor”, can better explain main information of influence of government affairs micro-blogs. The influence of government affairs micro-blogs can be divided into five types, which are“strength lead type”,“comprehensive medium type”,“comprehensive behind type”,“balanced development type” and“comprehensive lead type”. The results show that the levels of influence of government affairs micro-blogs are not balanced. Further, the distribution of influence of 16 government affairs micro-blogs presents a pyramidal structure.

作者赵阿敏曹桂全

机构地区天津大学管理与经济学部

出处《情报杂志》 CSSCI 北大核心 2014年第3期107-112,共6页 Journal of Intelligence

关键词政务微博微博影响力评价指标因子分析聚类分析 government affairs micro-blogs micro-blog influence evaluation index factor analysis cluster analysis

分类号 G350 [文化科学—情报学]

引文网络
相关文献

参考文献13

1金晓玲,汤振亚,周中允,燕京宏.微博客实证研究综述[J].情报杂志,2013,32(10):18-22. 被引量：5
22012年中国政务微博客评估报告[R],2013,3.
32013年上半年新浪政务微博报告[R].北京:人民网舆情监测室,2013.
4Jianshu Weng, En Peng Lira, Jing Jiang, et al. Twitterrank: Finding Topic-sensitive Influential Twitterers [ C ]. Third ACM International Conference on Web Search and Data Mining, 2010: 7-9.
5Danah Boyd, Scott Golder, Gilad Lotan. Tweet, Tweet, Retweet: Conversational Aspects of Retweeting on Twitter[ CJ. 43rd Ha- waii International Conference on Systems Sciences, 2010:4-10.
6李军,陈震,黄霁崴.微博影响力评价研究[J].信息网络安全,2012(3):10-13. 被引量：61
7Meeyoung Cha, Hamed Haddadi, Fabricio Benevenuto, et al. Measuring User Influence in Twitter: The Million Follower Fal- lacy[ C]. The Association for the Advancement of Artificial In- telligence, 2010 : 11 - 13.
8Shaozhi Ye, S. Felix Wu. Measuring Message Propagation and Social Influence on Twitter. com[C]. Proceedings of the 2nd In- ternational Conference on Social lnformatics, 2010:223-228.
9刘璐佳.微博影响力的直觉模糊评价方法[J].电脑知识与技术,2011,7(11X):8295-8296. 被引量：3
10刘清,彭赓,王苹.基于主成分分析法的微博影响力评估方法及实证分析[C].基于互联网的商业管理学术会议,2012:697-700.

二级参考文献73

1任一奇,王雅蕾,王国华,冯伟.微博谣言的演化机理研究[J].情报杂志,2012,31(5):50-54. 被引量：41
2Akshay Java et al., Why We Twitter: Understanding Microblogging Usage and Communities[C]. 9th WEBKDD and 1st SNA-KDD Workshop 2007.7-9.
3Dejin Zhao et al., How and Why People Twitter: The Role that Micro-blogging Plays in Informal Communication at Work[C]. GROUP 2009 of ACM. 247-249.
4Meeyoung Cha et al., Measuring User Influence in Twitter: The Million Follower Fallacy[C]. AAAI 2010. 11-13.
5Yuto Yamaguchi et al., TURank: Twitter User Ranking Based on User-Tweet Graph Analysis[C]. WISE 2010. 243-246.
6Jianshu Weng et al,. Twitterrank: Finding topic-sensitive influential twitterers[C]. WSDM 2010. 7-9.
7Eytan Bakshy et al., Everyone' s an Influencer: Quantifying Influence on Twitter[C]. WSDM 2011.67-69.
8D.J.Watts. Influentials, networks, and public opinion formation[J]. Journal of Consumer Research, 2007: 7-11.
9Shaozhi Ye, S. Felix Wu. Measuring Message Propagation and Social Influence on Twitter.com[C]. Soclnfo 2010. 223-228.
10Gabriel Weimann. The Influentials: People Who Influence People[M]. State University of New York Press, Albany, NY, 1994. 468-470.