Difference between revisions of "Lin et al KDD 2010"
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== Results == | == 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 | + | 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 because PET estimates the popularity by comprehensively considering historic, textual and structured information in a unified way. |
The popularity trend is shown in Fig.1. Network diffusion is shown in Fig.2. | The popularity trend is shown in Fig.1. Network diffusion is shown in Fig.2. | ||
[[File:Result.jpg]] | [[File:Result.jpg]] | ||
[[File:Result2.jpg]] | [[File:Result2.jpg]] | ||
Revision as of 13:53, 4 February 2011
This is a Paper I read in 10802 social media analysis.
Citation
PET：A Statistical Model for Popular Events Tracking in Social Communities
Authors
Cindy Xide Lin, Bo Zhao, Qiaozhu Mei, Jiawei Han
Problem
In this paper, the authors address a method to observe and track the popular events or topics that evolve over time in the communities. Existing methods separate topics and netword structures apart. In this paper, textual topics and network are combined together which makes more sense.
Method
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 because PET estimates the popularity by comprehensively considering historic, textual and structured information in a unified way.
The popularity trend is shown in Fig.1. Network diffusion is shown in Fig.2.
