Difference between revisions of "Lin et al KDD 2010"
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| − | Existing methods separate topics and netword structures apart. In this paper, | + | Existing methods separate topics and netword structures apart. In this paper, textual topics and network are combined together which makes more sense. The authors address the event tracking by first defining a term - [[UsesMethod::Popular Event Tracking]] (PET) in online communities which includes the popularity of events over time, the burstiness of user interest, information diffusion through the network structure and the evolution of topics. |
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Revision as of 13:57, 4 February 2011
This is a Paper I read in 10802 social media analysis.
Contents
Citation
PET：A Statistical Model for Popular Events Tracking in Social Communities Cindy Xide Lin, Bo Zhao, Qiaozhu Mei, Jiawei Han KDD, 2010
Problem
In this paper, the authors try to observe and track the popular events or topics that evolve over time in the communities.
Method
Existing methods separate topics and netword structures apart. In this paper, textual topics and network are combined together which makes more sense. The authors address the event tracking by first defining a term - Popular Event Tracking (PET) in online communities which includes the popularity of events over time, the burstiness of user interest, information diffusion through the network structure and the evolution of topics.
Dataset
In this paper, the authors select twitter as their source of data.
Results
Here in this paper, the authors compare PET with some other baseline models such as JonK, Cont, BOM and GInt. The authors apply these models to analyze both the Popularity Trend and Network diffusion. The result shows that PET generates the most consistent trends and the smoothest diffusion.
