Article

Mining correlated bursty topic patterns from coordinated text streams

Authors:

ChengXiang Zhai,

Richard SproatAuthors Info & Claims

KDD '07: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 784 - 793

https://doi.org/10.1145/1281192.1281276

Published: 12 August 2007 Publication History

Abstract

Previous work on text mining has almost exclusively focused on a single stream. However, we often have available multiple text streams indexed by the same set of time points (called coordinated text streams), which offer new opportunities for text mining. For example, when a major event happens, all the news articles published by different agencies in different languages tend to cover the same event for a certain period, exhibiting a correlated bursty topic pattern in all the news article streams. In general, mining correlated bursty topic patterns from coordinated text streams can reveal interesting latent associations or events behind these streams. In this paper, we define and study this novel text mining problem. We propose a general probabilistic algorithm which can effectively discover correlated bursty patterns and their bursty periods across text streams even if the streams have completely different vocabularies (e.g., English vs Chinese). Evaluation of the proposed method on a news data set and a literature data set shows that it can effectively discover quite meaningful topic patterns from both data sets: the patterns discovered from the news data set accurately reveal the major common events covered in the two streams of news articles (in English and Chinese, respectively), while the patterns discovered from two database publication streams match well with the major research paradigm shifts in database research. Since the proposed method is general and does not require the streams to share vocabulary, it can be applied to any coordinated text streams to discover correlated topic patterns that burst in multiple streams in the same period.

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Cited By

Ma CDu JLiang MGuan Z(2023)Topic Model Based on Co-Occurrence Word Networks for Unbalanced Short Text Datasets2023 5th International Conference on Data-driven Optimization of Complex Systems (DOCS)10.1109/DOCS60977.2023.10294993(1-7)Online publication date: 22-Sep-2023
https://doi.org/10.1109/DOCS60977.2023.10294993
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
Wang QWu JCui J(2022)Enhancing Heterogeneous Graph-based Short Text Topic Learning2022 IEEE Smartworld, Ubiquitous Intelligence & Computing, Scalable Computing & Communications, Digital Twin, Privacy Computing, Metaverse, Autonomous & Trusted Vehicles (SmartWorld/UIC/ScalCom/DigitalTwin/PriComp/Meta)10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00145(977-984)Online publication date: Dec-2022
https://doi.org/10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00145
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Index Terms

Mining correlated bursty topic patterns from coordinated text streams
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Clustering and classification
  2. Information systems applications
    1. Data mining
      1. Clustering

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Published In

cover image ACM Conferences

KDD '07: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining

August 2007

1080 pages

ISBN:9781595936097

DOI:10.1145/1281192

General Chair:
Pavel Berkhin
Yahoo!, USA
,
Program Chairs:
Rich Caruana
Cornell University, USA
,
Xindong Wu
University of Vermont, USA

Copyright © 2007 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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Publication History

Published: 12 August 2007

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KDD07

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KDD07: The 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 12 - 15, 2007

California, San Jose, USA

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KDD '07 Paper Acceptance Rate 111 of 573 submissions, 19%;

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

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Cited By

Ma CDu JLiang MGuan Z(2023)Topic Model Based on Co-Occurrence Word Networks for Unbalanced Short Text Datasets2023 5th International Conference on Data-driven Optimization of Complex Systems (DOCS)10.1109/DOCS60977.2023.10294993(1-7)Online publication date: 22-Sep-2023
https://doi.org/10.1109/DOCS60977.2023.10294993
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
Wang QWu JCui J(2022)Enhancing Heterogeneous Graph-based Short Text Topic Learning2022 IEEE Smartworld, Ubiquitous Intelligence & Computing, Scalable Computing & Communications, Digital Twin, Privacy Computing, Metaverse, Autonomous & Trusted Vehicles (SmartWorld/UIC/ScalCom/DigitalTwin/PriComp/Meta)10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00145(977-984)Online publication date: Dec-2022
https://doi.org/10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00145
Wang WCheng XLiu ZLin YShen YHu BZhang ZZeng XZhou JGu JLuo M(2022)Intent Mining: A Social and Semantic Enhanced Topic Model for Operation-Friendly Digital Marketing2022 IEEE 38th International Conference on Data Engineering (ICDE)10.1109/ICDE53745.2022.00308(3254-3267)Online publication date: May-2022
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Subbian KSridhar CAggarwal CSrivastava J(2022)Scalable Information Flow Mining in NetworksMachine Learning and Knowledge Discovery in Databases10.1007/978-3-662-44845-8_9(130-146)Online publication date: 10-Mar-2022
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Lind FEberl JEisele OHeidenreich TGalyga SBoomgaarden H(2021)Building the Bridge: Topic Modeling for Comparative ResearchCommunication Methods and Measures10.1080/19312458.2021.196597316:2(96-114)Online publication date: 7-Sep-2021
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Foorthuis R(2021)On the nature and types of anomalies: a review of deviations in dataInternational Journal of Data Science and Analytics10.1007/s41060-021-00265-112:4(297-331)Online publication date: 4-Aug-2021
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Singh TKumari M(2021)Burst: real-time events burst detection in social text streamThe Journal of Supercomputing10.1007/s11227-021-03717-4Online publication date: 22-Mar-2021
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Melanshiya BSwarna PFranklin R(2021)Twitter Topic Analysis Using Multi-tweet Sequential Summarization for Sentimental DataAdvances in Smart Grid and Renewable Energy10.1007/978-981-15-7511-2_54(547-554)Online publication date: 5-Jan-2021
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Feng KGuo TCong GBhowmick SMa S(2020)SURGE: Continuous Detection of Bursty Regions Over a Stream of Spatial ObjectsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2019.291565432:11(2254-2268)Online publication date: 1-Nov-2020
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