research-article

PET: a statistical model for popular events tracking in social communities

Authors:

Cindy Xide Lin,

Jiawei HanAuthors Info & Claims

KDD '10: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 929 - 938

https://doi.org/10.1145/1835804.1835922

Published: 25 July 2010 Publication History

Abstract

User generated information in online communities has been characterized with the mixture of a text stream and a network structure both changing over time. A good example is a web-blogging community with the daily blog posts and a social network of bloggers.

An important task of analyzing an online community is to observe and track the popular events, or topics that evolve over time in the community. Existing approaches usually focus on either the burstiness of topics or the evolution of networks, but ignoring the interplay between textual topics and network structures.

In this paper, we formally define the problem of popular event tracking in online communities (PET), focusing on the interplay between texts and networks. We propose a novel statistical method that models the the popularity of events over time, taking into consideration the burstiness of user interest, information diffusion on the network structure, and the evolution of textual topics. Specifically, a Gibbs Random Field is defined to model the influence of historic status and the dependency relationships in the graph; thereafter a topic model generates the words in text content of the event, regularized by the Gibbs Random Field. We prove that two classic models in information diffusion and text burstiness are special cases of our model under certain situations. Empirical experiments with two different communities and datasets (i.e., Twitter and DBLP) show that our approach is effective and outperforms existing approaches.

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Cited By

Han CSun X(2024)Enhancing Low Carbon Awareness in Social Media Discourse: A Fuzzy Clustering ApproachIEEE Access10.1109/ACCESS.2023.334812312(3200-3207)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2023.3348123
Pan YNik Hashim NGoh H(2024)Public perception of cultural ecosystem services in historic districts based on biterm topic modelScientific Reports10.1038/s41598-024-62770-014:1Online publication date: 22-May-2024
https://doi.org/10.1038/s41598-024-62770-0
Albrecht FWalter-Rogg M(2023)Der Einfluss bundespolitischer Themen auf den Landtagswahlkampf in Bayern 2018 – Eine Untersuchung der Twitter-Kommunikation von Bundes- und LandespolitikernDie Landtagswahl 2018 in Bayern10.1007/978-3-658-41392-7_11(417-452)Online publication date: 3-Aug-2023
https://doi.org/10.1007/978-3-658-41392-7_11
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Index Terms

PET: a statistical model for popular events tracking in social communities
1. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Conferences

KDD '10: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining

July 2010

1240 pages

ISBN:9781450300551

DOI:10.1145/1835804

General Chairs:
Bharat Rao
Siemens
,
Balaji Krishnapuram
Siemens
,
Program Chairs:
Andrew Tomkins
Google Inc.
,
Qiang Yang
Hong Kong University of Science and Technology

Copyright © 2010 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 25 July 2010

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KDD '10: The 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

July 25 - 28, 2010

DC, Washington, USA

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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Pan YNik Hashim NGoh H(2024)Public perception of cultural ecosystem services in historic districts based on biterm topic modelScientific Reports10.1038/s41598-024-62770-014:1Online publication date: 22-May-2024
https://doi.org/10.1038/s41598-024-62770-0
Albrecht FWalter-Rogg M(2023)Der Einfluss bundespolitischer Themen auf den Landtagswahlkampf in Bayern 2018 – Eine Untersuchung der Twitter-Kommunikation von Bundes- und LandespolitikernDie Landtagswahl 2018 in Bayern10.1007/978-3-658-41392-7_11(417-452)Online publication date: 3-Aug-2023
https://doi.org/10.1007/978-3-658-41392-7_11
Qiang JQian ZLi YYuan YWu X(2022)Short Text Topic Modeling Techniques, Applications, and Performance: A SurveyIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2020.299248534:3(1427-1445)Online publication date: 1-Mar-2022
https://doi.org/10.1109/TKDE.2020.2992485
Ajinaja MAdetunmbi AUgwu CPopoola O(2022)Semantic similarity measure for topic modeling using latent Dirichlet allocation and collapsed Gibbs samplingIran Journal of Computer Science10.1007/s42044-022-00124-76:1(81-94)Online publication date: 8-Nov-2022
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Pei XMehta D(2022)Multidimensional racism classification during COVID-19: stigmatization, offensiveness, blame, and exclusionSocial Network Analysis and Mining10.1007/s13278-022-00967-912:1Online publication date: 7-Sep-2022
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Agarwal MBaranawal ASimmhan YGupta M(2021)Event Related Data Collection from Microblog StreamsDatabase and Expert Systems Applications10.1007/978-3-030-86475-0_31(319-331)Online publication date: 1-Sep-2021
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Yu YGu YCai YJing DWang D(2020)Track Related Bursty Topics in WeiboArtificial Intelligence and Robotics10.1007/978-3-030-56178-9_15(197-202)Online publication date: 11-Nov-2020
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