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数据分析与知识发现  2019, Vol. 3 Issue (2): 13-20    DOI: 10.11925/infotech.2096-3467.2018.0424
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
基于计划行为理论的微博用户转发行为影响因素研究*
席林娜,窦永香()
西安电子科技大学经济与管理学院 西安 710126
Examining Reposts of Micro-bloggers with Planned Behavior Theory
Linna Xi,Yongxiang Dou()
School of Economics and Management, Xidian University, Xi’an 710126, China
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摘要 

【目的】探究微博用户转发行为的影响因素。【方法】基于计划行为理论, 考虑微博所传达情感及微博平台的时间线机制对于用户转发行为的影响, 提出研究假设并对其进行验证。【结果】用户情感与微博情感相似度、粉丝量均对微博用户转发行为具有显著影响, 微博时间线机制对于用户转发行为几乎没有影响。【局限】对于用 户的登录时间采用统一时间节点。【结论】本研究结果对于网络舆情控制、个性化推荐、微博广告营销具有借鉴意义。

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席林娜
窦永香
关键词 计划行为理论LDA主题模型用户行为分析    
Abstract

[Objective] This paper tries to explore the influencing factors of Microblog (Weibo) user’s reposting behaviors. [Methods] Based on the theory of planned behavior, we evaluted the sentiment of Weibo users and the impacts of the Weibo timeline on users’ reposting behaviors. [Results] The degree of similarity between the real world and online sentiments of Weibo users’, as well as the number of followers had significant impacts on Weibo user’s reposting behaviors. The timeline feature posed little effect to the user’s reposting behaviors. [Limitations] Only examined users logging in Weibo at a specific time. [Conclusions] This study could improve the performance of public opinion management, personalized recommendation, and advertising campains on Weibo.

Key wordsTheory of Planned Behavior    LDA Topic Model    User Behavior Analysis
收稿日期: 2018-04-20     
基金资助:*本文系国家自然科学基金青年项目“基于公众网络参与的民生公共政策第三方动态评估机理与方法研究”(项目编号: 71503195)和陕西省高校人文社会科学青年英才支持计划项目“社会化媒体中用户群体行为模式的发现与演化研究”(项目编号: ER42015060001)的研究成果之一
引用本文:   
席林娜,窦永香. 基于计划行为理论的微博用户转发行为影响因素研究*[J]. 数据分析与知识发现, 2019, 3(2): 13-20.
Linna Xi,Yongxiang Dou. Examining Reposts of Micro-bloggers with Planned Behavior Theory. Data Analysis and Knowledge Discovery, DOI:10.11925/infotech.2096-3467.2018.0424.
链接本文:  
http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2018.0424
[1] 中国互联网络信息中心. 第42次中国互联网发展状况统计报告[R]. 北京: 中国互联网络信息中心, 2018.
[1] (China Internet Network Information Center. The 42nd Statistical Report on Internet Development in China[R]. Beijing: China Internet Network Information Center, 2018. )
[2] Xu Z, Zhang Y, Wu Y, et al.Modeling User Posting Behavior on Social Media[C]// Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 2012: 545-554.
[3] Wang C, Zhou Z, Jin X, et al.The Influence of Affective Cues on Positive Emotion in Predicting Instant Information Sharing on Microblogs: Gender as a Moderator[J]. Information Processing and Management, 2017, 53(3): 721-734.
[4] Song G, Li Z, Tu H.Forward or Ignore: User Behavior Analysis and Prediction on Microblogging[C]// Proceeding of the 25th International Conference on Industrial Engineering and Other Applications of Applied Intelligent System. 2012: 231-241.
[5] 陈姝, 窦永香, 张青杰. 基于理性行为理论的微博用户转发行为影响因素研究[J]. 情报杂志, 2017, 36(11): 147-152.
[5] (Chen Shu, Dou Yongxiang, Zhang Qingjie.Research on the Influential Factors of the Reposting Behavior of Microblog Users Based on the Theory of Reasoned Action[J]. Journal of Intelligence, 2017, 36(11): 147-152.)
[6] 李志清. 基于LDA主题特征的微博转发预测[J]. 情报杂志, 2015, 34(9): 158-162.
[6] (Li Zhiqing.Predicting Retweeting Behavior Based on LDA Topic Features[J]. Journal of Intelligence, 2015, 34(9): 158-162.)
[7] 曹玖新, 吴江林, 石伟, 等. 新浪微博网信息传播分析与预测[J]. 计算机学报, 2014, 37(4): 780-789.
[7] (Cao Jiuxin, Wu Jianglin, Shi Wei, et al.Sina Microblog Information Diffusion Analysis and Prediction[J]. Chinese Journal of Computers, 2014, 37(4): 780-789.)
[8] 王晰巍, 邢云菲, 赵丹, 等. 基于社会网络分析的移动环境下网络舆情信息传播研究——以新浪微博“雾霾”话题为例[J]. 图书情报工作, 2015, 59(7): 14-22.
[8] (Wang Xiwei, Xing Yunfei, Zhao Dan, et al.The Study of Network Public Opinion Dissemination with Social Network Analysis Under the Mobile Environment: A Case of “Haze” in Sina Micro- blog[J]. Library and Information Service, 2015, 59(7): 14-22.)
[9] 郭淼, 焦垣生. 网络舆情传播与演变背景下的微博信息转发预测分析[J]. 情报杂志, 2016, 35(5): 46-51.
[9] (Guo Miao, Jiao Yuansheng.Predictive Analysis of Micro-blog Information Forwarding Under the Background of Network Public Opinion Dissemination and Evolution[J]. Journal of Intelligence, 2016, 35(5): 46-51.)
[10] 李倩, 张碧君, 赵中英. 微博信息转发影响因素研究[J]. 软件导刊, 2017, 16(1): 15-17.
[10] (Li Qian, Zhang Bijun, Zhao Zhongying.Research on the Influencing Factors of Microblog Information[J]. Software Guide, 2017, 16(1): 15-17.)
[11] 刘继, 李磊. 基于微博用户转发行为的舆情信息传播模式分析[J]. 情报杂志, 2013, 32(7): 74-77.
[11] (Liu Ji, Li Lei.Analysis of Public Opinion Propagation Mode Based on Repost Behavior of Microblog Users[J]. Journal of Intelligence, 2013, 32(7): 74-77.)
[12] Suh B, Hong L, Pirolli P, et al.Want to be Retweeted? Large Scaleanalytics on Factors Impacting Retweet in Twitter Network[C]// Proceedings of the 2nd International Conference on Social Computing. IEEE, 2010: 177-184.
[13] 唐晓波, 罗颖利. 融入情感差异和用户兴趣的微博转发预测[J]. 图书情报工作, 2017, 61(9): 102-110.
[13] (Tang Xiaobo, Luo Yingli.Integrating Emotional Divergence and User Interests into the Prediction of Microblog Retweeting[J]. Library and Information Service, 2017, 61(9): 102-110.)
[14] 李英乐, 于洪涛, 刘力雄. 基于SVM的微博转发规模预测方法[J]. 计算机应用研究, 2013, 30(9): 2594-2597.
[14] (Li Yingle, Yu Hongtao, Liu Lixiong.Predict Algorithm of Micro-Blog Retweet Scale Based on SVM[J]. Application Research of Computers, 2013, 30(9): 2594-2597.)
[15] Ajzen I.From Intentions to Actions: A Theory of Planned Behavior[A]// Kuhl J, Beckmann J. Action Control: From Cognition to Behavior[M]. New York: Springer-Verlag, 1985: 11-39.
[16] Madden T J, Ellen P S, Ajzen I.A Comparison of the Theory of Planned Behavior and the Theory of Reasoned Action[J]. Personality and Social Psychology Bulletin, 1992, 18(1): 3-9.
[17] Ajzen I.Theroy of Planned Behavior[J]. Organizational Behavior and Human Decision Processes, 1991, 50(2): 179-217.
[18] 顾东晓, 孙建军, 张悦, 等. 基于计划行为理论的用户链接分享研究[J]. 运筹与管理, 2016, 25(2): 173-179.
[18] (Gu Dongxiao, Sun Jianjun, Zhang Yue, et al.An Empirical Study on Internet Users’ Links Sharing Based on Planned Behavior Theory[J]. Operations Research and Management Science, 2016, 25(2): 173-179.)
[19] 李颖琦, 王宇露. 基于修正计划行为理论的大型企业虚拟学习社区知识共享研究——来自212个虚拟学习社区的实证[J]. 情报杂志, 2010, 29(5): 48-53.
[19] (Li Yingqi, Wang Yulu.A Research on Knowledge Sharing in Sizeable Enterprise’s Virtual Learning Community Based on Adjusted Theory of Planned Behavior[J]. Journal of Intelligence, 2010, 29(5): 48-53.)
[20] 王星辰. 社会化问答网站知识共享影响因素研究——基于计划行为理论[D]. 合肥: 中国科学技术大学, 2017.
[20] (Wang Xingchen.Research on the Factors Affecting Knowledge Sharing of Social Q&A Websites Based on the Theory of Planned Behavior[D]. Hefei: University of Science and Technology of China, 2017.)
[21] Saeri A K, Ogilvie C, La Macchia S T, et al. Predicting Facebook Users’ Online Privacy Protection: Risk, Trust, Norm Focus Theory, and the Theory of Planned Behavior[J]. The Journal of Social Psychology, 2014, 154(4): 352-369.
[22] 刘敏, 王莉. 社交网络中微博用户行为的分析与预测[J]. 太原理工大学学报, 2016, 47(6): 786-792.
[22] (Liu Min, Wang Li.Analysis and Prediction of Microblog User Behavior on Social Networks[J]. Journal of Taiyuan University of Technology, 2016, 47(6): 786-792.)
[23] Weng J, Lim E P, Jiang J, et al.TwitterRank: Finding Topic-sensitive Influential Twitters[C]// Proceedings of the 3rd ACM International Conference on Web Search and Data Ming. 2010: 261-270.
[24] Wang C, Jin X L, Lee M K O, et al. Understanding Status Update in Microblog: A Perspective on Media Needs[C]// Proceedings of the 17th Pacific Asia Conference on Information Systems, 2013.
[25] Huang J, Cheng X, Shen H, et al.Exploring Social Influence via Posterior Effect of Word-of-Mouth Recommendations[C]]// Proceedings of the 5th ACM International Conference on Web Search and Data Mining. 2012: 573-582.
[26] 廖海涵, 靳嘉林, 王曰芬. 网络舆情事件中微博用户行为特征和关系分析——以新浪微博“雾霾调查: 穹顶之下”为例[J]. 情报资料工作, 2016(3): 12-17.
[26] (Liao Haihan, Jin Jialin, Wang Yuefen.Behavioral Characteristics and Relationship Analysis of Weibo Users in Internet Public Opinion Event: Taking “Smog Investigation: Under the Dome” of Sina Weibo as Example[J]. Information and Documentation Services, 2016(3): 12-17.)
[27] Endres D M, Schindelin J E.A New Metric for Probability Distributions[J]. IEEE Transactions on Informations Theory, 2003, 49(7): 1858-1860.
[28] 刘玮, 贺敏, 王丽宏, 等. 基于用户行为特征的微博转发预测研究[J]. 计算机学报, 2016, 39(10): 1992-2005.
[28] (Liu Wei, He Min, Wang Lihong, et al.Research on Microblog Retweeting Prediction Based on User Behavior Features[J]. Chinese Journal of Computers, 2016, 39(10): 1992-2005.)
[29] Kawk H, Lee C, Park H, et al.What is Twitter, a Social Network or a News Media?[C]// Proceedings of the 19th International Conference on World Wide Web. ACM, 2010: 591-600.
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