Abstract
As one kind of social media, microblogs are widely used for sensing the real-world. The popularity of microblogs is an important measurement for evaluation of the influencial of pieces of information. The models and modeling techniques for popularity of microblogs are studied in this paper. A huge data set based on Sina Weibo, one of the most popular microblogging services, is used in the study. First, two different types of popularity, namely number of retweets and number of possible views are defined, while their relationships are discussed. Then, the temporal dynamics, including lifecycles and tipping-points, of tweets’ popularity are studied. For modeling the temporal dynamics, a piecewise sigmoid model is used. Empirical studies show the effectiveness of our modeling methods.
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Haixin MA received the BS in Software Engineering from East China Normal University in 2007 and is currently a master student there. Her research interest is social media data analysis. She is currently working on modeling and predicting the popularity of the microblogs.
Weining QIAN is currently a professor in Computer Science at East China Normal University, China. He received his MS and PhD in Computer Science from Fudan University in 2001 and 2004, respectively. He is supported by National Natural Science Foundation of China (NSFC) under Grant No. 61170086. He served as the co-chair of WISE 2012 Challenge, and program committee member of several international conferences, including ICDE 2009/2010/2012 and KDD 2013. His research interests include Web data management and mining of massive data sets.
Fan XIA received the BS in Software Engineer from East China Normal University in 2006 and is currently a PhD student there. His research interests include query optimization in MapReduce, and large scale database systems. He is currently working on developing a novel cache mechanism to manage timeline data in social network system.
Xiaofeng HE is with Software Engineering Institute, East China Normal University (ECNU). He obtained his PhD from Pennsylvania State University. Xiaofeng’s current interests include computation advertise, clustering and classification, Web search, learning to rank. Prior to joining ECNU, Xiaofeng worked at Microsoft, Yahoo Labs and Lawrence Berkeley National Laboratories.
Jun (Jim) XU is a professor in the College of Computing at Georgia Institute of Technology. He received PhD in Computer and Information Science from The Ohio State University in 2000. His current research interests include data streaming algorithms for the measurement and monitoring of computer networks, and algorithms and data structures for high-speed routers. He received the NSF CAREER award in 2003, ACM Sigmetrics best student paper award in 2004, and IBM faculty awards in 2006 and 2008. He was named an ACM Distinguished Scientist in 2010.
Aoying ZHOU, professor on computer science at East China Normal University (ECNU), where he is heading the Institute of Massive Computing. He got his master and bachelor degree in computer science from Sichuan University, in 1988 and 1985 respectively, and won his PhD degree from Fudan University in 1993. Before joining ECNU in 2008, he worked for Fudan University at the Computer Science Department from 1993 to 2007, where he served as the department chair from 1999 to 2002. He worked as a visiting scholar under the Berkeley Scholar Program in UC Berkeley in 2005. He is the winner of the National Science Fund for Distinguished Young Scholars supported by NSFC and the professorship appointment under Changjiang Scholars Program of Ministry of Education. He is now acting as the vice-director of ACMSIGMOD China and Technology Committee on Database of China Computer Federation. He is serving as member of the editorial boards of some prestigious academic journals, such as VLDB Journal, www Journal. His research interests include Web data management, data management for data-intensive computing, in-memory data analytics.
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Ma, H., Qian, W., Xia, F. et al. Towards modeling popularity of microblogs. Front. Comput. Sci. 7, 171–184 (2013). https://doi.org/10.1007/s11704-013-3901-9
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DOI: https://doi.org/10.1007/s11704-013-3901-9