Difference between revisions of "Lin et al KDD 2010"
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In this paper, the authors try to [[AddressesProblem::observe and track the popular events or topics]] that evolve over time in the communities. | In this paper, the authors try to [[AddressesProblem::observe and track the popular events or topics]] that evolve over time in the communities. | ||
| − | == | + | == Summary == |
Existing methods separate topics and network structure apart. In this paper, textual topics and network are combined together which makes more sense. The authors address the event tracking by using a model - [[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. | Existing methods separate topics and network structure apart. In this paper, textual topics and network are combined together which makes more sense. The authors address the event tracking by using a model - [[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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In this paper, the authors select [[UsesDataset::twitter]] as their source of data. | In this paper, the authors select [[UsesDataset::twitter]] as their source of data. | ||
| − | == | + | == Evaluation == |
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. | 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. | ||
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| + | == Related Papers == | ||
| + | |||
| + | [1] L.A.Adamic and E.Adar. [[RelatedPaper::Friends and neighbors on the web]]. | ||
| + | |||
| + | [2] L.Araujo, J.A.Cuesta. [[RelatedPaper::Genetic algorithm for burst detection and activity tracking in event streams]]. | ||
Latest revision as of 23:27, 6 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 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.
Summary
Existing methods separate topics and network structure apart. In this paper, textual topics and network are combined together which makes more sense. The authors address the event tracking by using a model - 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.
Evaluation
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.
Related Papers
[1] L.A.Adamic and E.Adar. Friends and neighbors on the web.
[2] L.Araujo, J.A.Cuesta. Genetic algorithm for burst detection and activity tracking in event streams.
