research-article

Bursty Event Detection in Twitter Streams

Authors:

Carmela Comito,

Agostino Forestiero,

Clara PizzutiAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 13, Issue 4

Article No.: 41, Pages 1 - 28

https://doi.org/10.1145/3332185

Published: 08 August 2019 Publication History

Abstract

Social media, in recent years, have become an invaluable source of information for both public and private organizations to enhance the comprehension of people interests and the onset of new events. Twitter, especially, allows a fast spread of news and events happening real time that can contribute to situation awareness during emergency situations, but also to understand trending topics of a period. The article proposes an online algorithm that incrementally groups tweet streams into clusters. The approach summarizes the examined tweets into the cluster centroid by maintaining a number of textual and temporal features that allow the method to effectively discover groups of interest on particular themes. Experiments on messages posted by users addressing different issues, and a comparison with state-of-the-art approaches show that the method is capable to detect discussions regarding topics of interest, but also to distinguish bursty events revealed by a sudden spreading of attention on messages published by users.

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Cited By

Lu XZhou XGan SHe XChen XXiao YLiu Y(2025)SAEQ: Semantic anomaly event quantifier for event detection and judgement in social mediaExpert Systems with Applications10.1016/j.eswa.2025.126522(126522)Online publication date: Jan-2025
https://doi.org/10.1016/j.eswa.2025.126522
Nagashima HTajima KChua TNgo CKumar RLauw HKa-Wei Lee R(2024)Automatic Construction of Expiration Time Expression Dataset from RetweetsCompanion Proceedings of the ACM Web Conference 202410.1145/3589335.3651471(545-548)Online publication date: 13-May-2024
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Sharma SAbulaish MAhmad T(2024)conteNXt: A Graph-Based Approach to Assimilate Content and Context for Event Detection in OSNIEEE Transactions on Computational Social Systems10.1109/TCSS.2024.337239911:4(5483-5495)Online publication date: Aug-2024
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Index Terms

Bursty Event Detection in Twitter Streams
1. Information systems
  1. Information systems applications
    1. Data mining
      1. Clustering
      2. Data stream mining
    2. Decision support systems
      1. Online analytical processing
  2. World Wide Web
    1. Web applications
      1. Internet communications tools
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Published In

cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 13, Issue 4

August 2019

235 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3343141

Editors:
Charu Aggarwal
IBM T. J. Watson Research, USA
,
Xindong Wu
University of Louisiana at Lafayette, USA

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Copyright © 2019 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 August 2019

Accepted: 01 May 2019

Revised: 01 March 2019

Received: 01 August 2018

Published in TKDD Volume 13, Issue 4

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Cited By

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https://doi.org/10.1016/j.eswa.2025.126522
Nagashima HTajima KChua TNgo CKumar RLauw HKa-Wei Lee R(2024)Automatic Construction of Expiration Time Expression Dataset from RetweetsCompanion Proceedings of the ACM Web Conference 202410.1145/3589335.3651471(545-548)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589335.3651471
Sharma SAbulaish MAhmad T(2024)conteNXt: A Graph-Based Approach to Assimilate Content and Context for Event Detection in OSNIEEE Transactions on Computational Social Systems10.1109/TCSS.2024.337239911:4(5483-5495)Online publication date: Aug-2024
https://doi.org/10.1109/TCSS.2024.3372399
Li LVásárhelyi OVedres B(2024)Social bots spoil activist sentiment without eroding engagementScientific Reports10.1038/s41598-024-74032-014:1Online publication date: 6-Nov-2024
https://doi.org/10.1038/s41598-024-74032-0
Vijayakumar ARajam V(2024)Detection and context reconstruction of sub-events that influence the course of a news event from microblog discussionsJournal of Computational Social Science10.1007/s42001-024-00279-27:2(1483-1517)Online publication date: 26-Apr-2024
https://doi.org/10.1007/s42001-024-00279-2
Xiao XDu MXu SLiu GZhang C(2024)Cross-media web video event mining based on multiple semantic-paths embeddingNeural Computing and Applications10.1007/s00521-023-09050-636:2(667-683)Online publication date: 1-Jan-2024
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Goswami AKumar APramod D(2024)Bursty Event Detection Model for TwitterDistributed Computing and Intelligent Technology10.1007/978-3-031-50583-6_23(338-355)Online publication date: 17-Jan-2024
https://dl.acm.org/doi/10.1007/978-3-031-50583-6_23
Montero MEstuar M(2023)Exploring Event-based Dynamic Topic Modeling*2023 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC)10.1109/CyberC58899.2023.00036(165-174)Online publication date: 2-Nov-2023
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Hettiarachchi HAdedoyin-Olowe MBhogal JGaber M(2023)WhatsUpInformation Sciences: an International Journal10.1016/j.ins.2023.01.001625:C(553-577)Online publication date: 1-May-2023
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