Please wait a minute...
Data Analysis and Knowledge Discovery  2019, Vol. 3 Issue (4): 33-41    DOI: 10.11925/infotech.2096-3467.2018.1037
Current Issue | Archive | Adv Search |
Selection of Users’ Behaviors Towards Different Topics of Microblog on Public Health Emergencies
Lu An1,Yanping Liang2()
1Center for Studies of Information Resources, Wuhan University, Wuhan 430072, China
2School of Information Management, Wuhan University, Wuhan 430072, China
Download: PDF (730 KB)   HTML ( 6
Export: BibTeX | EndNote (RIS)      

[Objective] This paper aims to reveal the relationship between topics of microblog and user behaviors at different stages of public health emergencies. [Methods] We analyzed the behavioral patterns among different topics and within a specific topic. The LDA topic model improved by the relevance formula was employed to extract the topics of microblog entries on public health emergencies. The cosine distances between microblog topics and the numbers of retweets, comments, favorites, as well as those between each pair of behavior counts, were calculated to explore users’ behavior patterns towards the same or different topics. [Results] During public health emergencies, the evolutionary trends of users’ behaviors of retweets, comments, favorites are roughly similar. Significant correlations exist between the counts of three behaviors. The correlation coefficients between the counts of retweets and comments, those of comments and favorites, and those of retweets and favorites are 0.390, 0.274, 0.180 respectively. Microblogs related to the topics of event progress, government responses and knowledge dissemination are more likely to be commented on, while those related to the topics of public opinions and event measures are more likely to be retweeted. [Limitations] The universality of the conclusion is subject to the examination of other cases. [Conclusions] The tendency of user behaviors towards different types of topics is obviously unequal, which means different behaviors may happen among different topics and within a specific topic.

Key wordsMicroblog Topics      User Behavior      Opinion Evolution      Public Health Emergency      Lifecycle Model     
Received: 18 September 2018      Published: 29 May 2019

Cite this article:

Lu An,Yanping Liang. Selection of Users’ Behaviors Towards Different Topics of Microblog on Public Health Emergencies. Data Analysis and Knowledge Discovery, 2019, 3(4): 33-41.

URL:     OR

[1] Blei D M, Ng A Y, Jordan M I.Latent Dirichlet Allocation[J]. Journal of Machine Learning Research, 2003, 3: 993-1022.
[2] Liu X, Burns A C, Hou Y.An Investigation of Brand-Related User-Generated Content on Twitter[J]. Journal of Advertising, 2017, 46(2): 236-247.
[3] Panasyuk A, Yu E S L, Mehrotra K G. Controversial Topic Discovery on Members of Congress with Twitter[J]. Procedia Computer Science, 2014, 36: 160-167.
[4] Karami A, Dahl A A, Turner-Mcgrievy G, et al.Characterizing Diabetes, Diet, Exercise, and Obesity Comments on Twitter[J]. International Journal of Information Management, 2018, 38(1): 1-6.
[5] Zhao W X, Jiang J, Weng J, et al.Comparing Twitter and Traditional Media Using Topic Models[C]// Proceedings of the 33rd European Conference on Information Retrieval. 2011: 338-349.
[6] Ramage D, Dumais S T, Liebling D J.Characterizing Microblogs with Topic Models[C]// Proceedings of the 4th International AAAI Conference on Weblogs and Social Media. 2010: 130-137.
[7] Blei D M, Lafferty J D.Dynamic Topic Models[C]// Proceedings of the 23rd International Conference on Machine Learning. ACM, 2006: 113-120.
[8] Lau J H, Collier N, Baldwin T.On-line Trend Analysis with Topic Models: #twitter Trends Detection Topic Model Online[C]// Proceedings of the 2012 International Conference on Computational Linguistics. 2012: 1519-1534.
[9] Zhao H, Liu G, Shi C, et al.A Retweet Number Prediction Model Based on Followers’ Retweet Intention and Influence[C]// Proceedings of the 2014 IEEE International Conference on Data Mining Workshop. IEEE, 2015: 952-959.
[10] 马莹莹. 微博用户转发行为及情感预测研究[D]. 哈尔滨: 哈尔滨工业大学, 2015.
[10] (Ma Yingying.Research on Prediction of Retweeting Behaviors and Sentiment on Microblog[D]. Harbin: Harbin Institute of Technology, 2015.)
[11] 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.
[11] (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.)
[12] 孟吉杰. 突发事件中政务微博发布的实证研究——以“上海发布”典型案例为例[D]. 上海: 上海交通大学, 2014.
[12] (Meng Jijie.An Empirical Study on the Government Microblog Released in Emergencies —— “Shanghai City” Microblog as an Example[D]. Shanghai: Shanghai Jiaotong University, 2014.)
[13] Qu Y, Huang C, Zhang P, et al.Microblogging After a Major Disaster in China: A Case Study of the 2010 Yushu Earthquake[C]// Proceedings of the ACM 2011 Conference on Computer Supported Cooperative Work, 2011: 25-34.
[14] Burkholder B T, Toole M J.Evolution of Complex Disasters[J]. The Lancet, 1995, 346(8981): 1012-1015.
[15] Fink S.Crisis Management: Planning for the Inevitable[M]. New York: American Management Association, 1986: 20.
[16] Turner B A.The Organizational and Interorganizational Development of Disasters[J]. Administrative Science Quarterly , 1976, 21(3): 378-397.
[17] 易承志. 群体性突发事件网络舆情的演变机制分析[J]. 情报杂志, 2011, 30(12): 6-12.
[17] (Yi Chengzhi.Analysis on the Changing Mechanism of Mass Emergency Network Public Opinion[J]. Journal of Intelligence, 2011, 30(12): 6-12.)
[18] 贾亚敏, 安璐, 李纲. 城市突发事件网络信息传播时序变化规律研究[J]. 情报杂志, 2015, 34(4): 91-96, 90.
[18] (Jia Yamin, An Lu, Li Gang.On the Online Information Dissemination Pattern of City Emergencies[J]. Journal of Intelligence, 2015, 34(4): 91-96, 90.)
[19] 刘志明, 刘鲁. 面向突发事件的民众负面情绪生命周期模型[J]. 管理工程学报, 2013, 27(1): 15-21.
[19] (Liu Zhiming, Liu Lu.Public Negative Emotion Model in Emergencies Based on Aging Theory[J]. Journal of Industrial Engineering and Engineering Management, 2013, 27(1): 15-21.)
[20] Sievert C, Shirley K E.LDAvis: A Method for Visualizing and Interpreting Topics[C]// Proceedings of the Workshop on Interactive Language Learning, Visualization, and Interfaces. 2014: 63-70.
[21] Han J, Kamber M, Pei J.Data Mining Concepts and Techniques[M]. The 3rd Edition. New York: Morgan Kaufmann, 2012.
[22] R语言LDA可视化包LDAvis[EB/OL]. [2018-02-28]..
[22] (A LDA Visualization Package in R[EB/OL]. [2018-02-28]..)
[23] 王佳敏, 吴鹏, 陈芬, 等. 突发事件中意见领袖的识别和影响力实证研究[J].情报学报, 2016, 35(2): 169-176.
[23] (Wang Jiamin, Wu Peng, Chen Fen, et al.Empirical Study on Recognition and Influence of Opinion Leaders in Emergency[J]. Journal of the China Society for Scientific and Technical Information, 2016, 35(2): 169-176.)
[24] 易兰丽. 基于人类动力学的微博用户行为统计特征分析与建模研究[D]. 北京: 北京邮电大学, 2012.
[24] (Yi Lanli.Research on Statistical Characteristic Analysis and Modeling for User Behavior in Microblog Community Based on Human[D]. Beijing: Beijing University of Posts and Telecommunications, 2012.)
[25] 宋恩梅, 左慧慧. 新浪微博中的“权威”与“人气”: 以社会网络分析为方法[J]. 图书情报知识, 2012(3): 43-54.
[25] (Song Enmei, Zuo Huihui.Authority and Popularity: Social Network Analysis on Sina Microblogging[J]. Document, Information & Knowledge, 2012(3): 43-54.)
[26] 王晓光. 微博客用户行为特征与关系特征实证分析——以“新浪微博”为例[J]. 图书情报工作, 2010, 54(14): 66-70.
[26] (Wang Xiaoguang.Empirical Analysis on Behavior Characteristics and Relation Characteristics of Micro-blog Users——Take “Sina Micro-blog” for Example[J]. Library and Information Service, 2010, 54(14): 66-70.)
[27] Qu Y, Wu P F, Wang X.Online Community Response to Major Disaster: A Study of Tianya Forum in the 2008 Sichuan Earthquake[C]// Proceedings of the 42nd Hawaii International Conference on System Sciences. IEEE, 2009: 1-11.
[1] Zhang Yipeng,Ma Jingdong. Analyzing Sentiments and Dissemination of Misinformation on Public Health Emergency[J]. 数据分析与知识发现, 2020, 4(12): 45-54.
[2] Lin Wang,Ke Wang,Jiang Wu. Public Opinion Propagation and Evolution of Public Health Emergencies in Social Media Era: A Case Study of 2018 Vaccine Event[J]. 数据分析与知识发现, 2019, 3(4): 42-52.
[3] Linna Xi,Yongxiang Dou. Examining Reposts of Micro-bloggers with Planned Behavior Theory[J]. 数据分析与知识发现, 2019, 3(2): 13-20.
[4] Chen Yuan,Liu Fuzhen,Wu Jiang. Studying Users’ Interaction Behaviors of Sharing Economic Platform with 2-Mode Complex Network Analysis[J]. 数据分析与知识发现, 2017, 1(6): 72-82.
[5] Xia Lixin,Yang Jinqing,Cheng Xiufeng. Collecting Mobile Data Based on Content Awareness——An Empirical Study[J]. 数据分析与知识发现, 2017, 1(5): 82-93.
[6] Tong Guoping, Sun Jianjun. User Behavior Analysis Based on Search Engine Log[J]. 现代图书情报技术, 2015, 31(7-8): 80-88.
[7] Huang Wenbin, Xu Shanchuan, Ma Long, Wang Jun. Analysis of Mobile User Behaviors with Telecommunication Data[J]. 现代图书情报技术, 2015, 31(5): 80-87.
[8] Yang Ning, Huang Feihu, Wen Yi, Chen Yunwei. An Opinion Evolution Model Based on the Behavior of Micro-blog Users[J]. 现代图书情报技术, 2015, 31(12): 34-41.
[9] Chen Yong, Li Honglian, Lv Xueqiang. Analysis for the Search Behavior of Web Users[J]. 现代图书情报技术, 2014, 30(12): 10-17.
[10] Yang Liu, Zhu Hengmin, Ma Jing. Evolution Model of Microblog Public Opinion Considering the Influence of Next-nearest Neighbors[J]. 现代图书情报技术, 2014, 30(12): 78-84.
[11] He Jing, Guo Jinli, Xu Xuejuan. Analysis on Statistical Characteristic and Dynamics for User Behavior in Microblog Communities[J]. 现代图书情报技术, 2013, 29(7/8): 94-100.
[12] Qiu Jin, Wu Dan. A Study on Users’ Collaborative Information Seeking Behavior and System Evaluation——A Perspective of Tasks and Collaborative Abilities[J]. 现代图书情报技术, 2012, (9): 62-68.
[13] Zhang Yunzhong. Using Formal Concept Analysis to Construct the Model of User Behavior Knowledge Discovery in Folksonomy[J]. 现代图书情报技术, 2012, 28(7): 66-75.
[14] Yuan Yuan, Sun Xiaoling, Zhu Qinghua. Research on Attention Behavior of Microblog Users Based on Social Network Analysis[J]. 现代图书情报技术, 2012, 28(2): 68-75.
[15] Guo Xiaoqing, Ren Shougang, Xie Zhonghong. Research and Implementation of Drive-level Local User Behavior Monitoring System[J]. 现代图书情报技术, 2012, (10): 77-82.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938