The Mathematics of Infectious Diseases
SIAM Review
On the bursty evolution of blogspace
WWW '03 Proceedings of the 12th international conference on World Wide Web
Information diffusion through blogspace
Proceedings of the 13th international conference on World Wide Web
Topic Detection from Blog Documents Using Users' Interests
MDM '06 Proceedings of the 7th International Conference on Mobile Data Management
Topic sentiment mixture: modeling facets and opinions in weblogs
Proceedings of the 16th international conference on World Wide Web
Exploring in the weblog space by detecting informative and affective articles
Proceedings of the 16th international conference on World Wide Web
Mining correlated bursty topic patterns from coordinated text streams
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Blog Community Discovery and Evolution Based on Mutual Awareness Expansion
WI '07 Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence
Proceedings of the hypertext 2008 workshop on Collaboration and collective intelligence
Environmental Modelling & Software
Multi-scale characterization of social network dynamics in the blogosphere
Proceedings of the 17th ACM conference on Information and knowledge management
Analyzing communities and their evolutions in dynamic social networks
ACM Transactions on Knowledge Discovery from Data (TKDD)
Getting insights from the voices of customers: Conversation mining at a contact center
Information Sciences: an International Journal
How valuable is medical social media data? Content analysis of the medical web
Information Sciences: an International Journal
Exploiting noun phrases and semantic relationships for text document clustering
Information Sciences: an International Journal
Information discovery across multiple streams
Information Sciences: an International Journal
Sliding window-based frequent pattern mining over data streams
Information Sciences: an International Journal
Propagation Modeling and Analysis of Incidental Topics in Blogosphere
OCSC '09 Proceedings of the 3d International Conference on Online Communities and Social Computing: Held as Part of HCI International 2009
Detecting Changes over Time in a Knowledge Sharing Community
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Making sense of meaning: leveraging social processes to understand media semantics
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Global analysis of an epidemic model with a constant removal rate
Mathematical and Computer Modelling: An International Journal
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Modeling the propagation of hot online topic is a preliminary requirement of predicting the trend of hot online topic. We propose a time-varying hot topic propagation model in online discussion context based upon the collective behavior of users who are in different social subgroups on blog networks and bulletin board system (BBS) sites. By analyzing the stability of the equilibrium of our model, we search for the threshold to be watershed of the trend of hot online topic and generalize about two theorems from the results of analysis, they exposit two sufficient conditions under which the trend of hot online topic will die out or remain uniformly weakly persistent. Furthermore, we propose methods to predict the trend of hot online topic on the strength of our model and theorems. For different motivation, we design two methods: Method (I) is mainly served as a way of theoretical research for predicting long trend of single-peak hot online topic by the thresholds of theorems; and for application, we design method (II) to predict the number of users writing or commenting upon article posts with respect to multi-peak hot online topic and single-peak one in the following two days with the help of Method (I). Experiments of two methods are performed on widely-discussed topics on the Sina Blog and the famous Liang Quan Qi Mei (LQQM) BBS and Xi'an Jiaotong University (BMY) BBS in China. The experimental results show that our methods predict the trend of hot online topic efficiently not only for theoretical motivation but also for applicable motivation, and reduce the computational complexity. Hence, our model can serve as basis for predicting trends in hot online topic propagation.