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.

References

[1]

Hamed Abdelhaq, Christian Sengstock, and Michael Gertz. 2013. EvenTweet: Online localized event detection from Twitter. Proceedings of the VLDB Endowment 6, 12 (Aug. 2013), 1326--1329.

Digital Library

[2]

Charu C. Aggarwal and Philip S. Yu. 2010. On clustering massive text and categorical data streams. Knowledge and Information Systems 24, 2 (2010), 171--196.

Digital Library

[3]

Rakesh Agrawal and Ramakrishnan Srikant. 1994. Fast algorithms for mining association rules in large databases. In Proceedings of the 20th International Conference on Very Large Data Bases (VLDB’94). 487--499.

Digital Library

[4]

Luca Maria Aiello, Georgios Petkos, Carlos Martin, David Corney, Symeon Papadopoulos, Ryan Skraba, Ayse Goker, Ioannis Kompatsiaris, and Alejandro Jaimes. 2013. Sensing trending topics in Twitter. IEEE Transactions on Multimedia 15, 6 (2013), 1268--1282.

Digital Library

[5]

James Allan. 2002. Topic Detection and Tracking: Event-based Information Organization. Kluwer Academic Publisher, Norwel, MA.

Digital Library

[6]

James Allan, Jaime Carbonell, George Doddington, Jonathan Yamron, Yiming Yang, James Allan Umass, Brian Archibald Cmu, Doug Beeferman Cmu, Adam Berger Cmu, Ralf Brown Cmu, Ira Carp Dragon, George Doddington Darpa, Alex Hauptmann Cmu, John Lafferty Cmu, Victor Lavrenko Umass, Xin Liu Cmu, Steve Lowe Dragon, Paul Van Mulbregt Dragon, Ron Papka Umass, Thomas Pierce Cmu, Jay Ponte Umass, and Mike Scudder Umass. 1998. Topic detection and tracking pilot study final report. In Proceedings of the DARPA Broadcast News Transcription and Understanding Workshop. 194--218.

[7]

Nasser Alsaedi, Pete Burnap, and Omer Rana. 2017. Can we predict a riot? Disruptive event detection using Twitter. ACM Transactions on Internet Technology 17, 2 (Mar. 2017), 18:1--18:26.

Digital Library

[8]

Farzindar Atefeh and Wael Khreich. 2015. A survey of techniques for event detection in Twitter. Computational Intelligence 31, 1 (Feb. 2015), 132--164.

Digital Library

[9]

Hila Becker, Mor Naaman, and Luis Gravano. 2011. Beyond trending topics: Real-world event identification on Twitter. In Proceedings of the 5th International Conference on Weblogs and Social Media.

[10]

David M. Blei, Andrew Y. Ng, Michael I. Jordan, and John Lafferty. 2003. Latent Dirichlet allocation. Journal of Machine Learning Research 3 (2003), 2003.

Digital Library

[11]

Mário Cordeiro and João Gama. 2016. Online Social Networks Event Detection: A Survey. Springer International Publishing, Cham, 1--41.

[12]

Ingo Feinerer, Kurt Hornik, and David Meyer. 2008. Text mining infrastructure in R. Journal of Statistical Software 25, 5 (31 Mar. 2008), 1--54.

[13]

Gabriel Pui Cheong Fung, Jeffrey Xu Yu, Philip S. Yu, and Hongjun Lu. 2005. Parameter free bursty events detection in text streams. In Proceedings of the 31st International Conference on Very Large Data Bases (VLDB’05). 181--192.

Digital Library

[14]

Anuradha Goswami and Ajey Kumar. 2016. A survey of event detection techniques in online social networks. Social Network Analysis and Mining 6, 1 (17 Nov. 2016), 107.

[15]

Adrien Guille and Cécile Favre. 2015. Event detection, tracking, and visualization in Twitter: A mention-anomaly-based approach. Social Network Analysis and Mining 5, 1 (2015), 18:1--18:18.

[16]

Mahmud Hasan, Mehmet A. Orgun, and Rolf Schwitter. 2018. A survey on real-time event detection from Twitter data stream. Journal of Information Science 44, 4 (2018), 443--463.

Digital Library

[17]

Muhammad Imran, Carlos Castillo, Fernando Diaz, and Sarah Vieweg. 2015. Processing social media messages in mass emergency: A survey. ACM Computing Survey 47, 4, Article 67 (Jun. 2015), 38 pages.

Digital Library

[18]

Thorsten Joachims. 1998. Text categorization with support vector machines: Learning with many relevant features. In Proceedings of the 10th European Conference on Machine Learning (ECML’98). 137--142.

Digital Library

[19]

Jon Kleinberg. 2003. Bursty and hierarchical structure in streams. Data Mining and Knowledge Discovery 7, 4 (2003), 373--397.

Digital Library

[20]

Donald E. Knuth. 1997. The Art of Computer Programming, Volume 2 (3rd ed.): Seminumerical Algorithms. Addison-Wesley Longman Publishing Co., Inc., Boston, MA.

Digital Library

[21]

Sungjun Lee, Sangjin Lee, Kwanho Kim, and Jonghun Park. 2012. Bursty event detection from text streams for disaster management. In Proceedings of the 21st International Conference on World Wide Web (WWW’12 Companion). ACM, New York, NY, 679--682.

Digital Library

[22]

Chenliang Li, Aixin Sun, and Anwitaman Datta. 2012. Twevent: Segment-based event detection from tweets. In Proceedings of the 21st ACM International Conference on Information and Knowledge Management (CIKM’12), Maui, HI, October 29--November 02, 2012. 155--164.

Digital Library

[23]

Jianxin Li, Zhenying Tai, Richong Zhang, Weiren Yu, and Lu Liu. 2014. Online bursty event detection from microblog. In Proceedings of the 2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing (UCC’14). IEEE Computer Society, 865--870.

Digital Library

[24]

Michael Mathioudakis and Nick Koudas. 2010. TwitterMonitor: Trend detection over the Twitter stream. In Proceedings of the 2010 ACM SIGMOD International Conference on Management of Data (SIGMOD’10). 1155--1158.

Digital Library

[25]

Qiaozhu Mei and ChengXiang Zhai. 2005. Discovering evolutionary theme patterns from text: An exploration of temporal text mining. In Proceedings of the 11th ACM SIGKDD International Conference on Knowledge Discovery in Data Mining (KDD’05). ACM, New York, NY, 198--207.

Digital Library

[26]

Fionn Murtagh. 1983. A survey of recent advances in hierarchical clustering algorithms. Computer Journal 26, 4 (1983), 354--359.

[27]

Ruchi Parikh and Kamalakar Karlapalem. 2013. ET: Events from tweets. In Proceedings of the 22nd International Conference on World Wide Web (WWW’13 Companion). ACM, New York, NY, 613--620.

Digital Library

[28]

Saša Petrović, Miles Osborne, and Victor Lavrenko. 2010. Streaming first story detection with application to Twitter. In Proceedings of the Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics. Association for Computational Linguistics, 181--189.

Digital Library

[29]

Daniela Pohl, Abdelhamid Bouchachia, and Hermann Hellwagner. 2012. Automatic sub-event detection in emergency management using social media. In Proceedings of the 21st International Conference on World Wide Web (WWW’12 Companion). ACM, New York, NY, 683--686.

Digital Library

[30]

Takeshi Sakaki, Makoto Okazaki, and Yutaka Matsuo. 2013. Tweet analysis for real-time event detection and earthquake reporting system development. IEEE Transactions on Knowledge and Data Engineering 25, 4 (April 2013), 919--931.

Digital Library

[31]

Gerard Salton and Michael J. McGill. 1986. Introduction to Modern Information Retrieval. McGraw-Hill, Inc., New York, NY.

Digital Library

[32]

Jonathan A. Silva, Elaine R. Faria, Rodrigo C. Barros, Eduardo R. Hruschka, André C. P. L. F. de Carvalho, and João Gama. 2013. Data stream clustering: A survey. ACM Computing Surveys 46, 1, Article 13 (Jul. 2013), 13:1--13:31 pages.

Digital Library

[33]

Giovanni Stilo and Paola Velardi. 2016. Efficient temporal mining of micro-blog texts and its application to event discovery. Data Mining and Knowledge Discovery 30, 2 (Mar. 2016), 372--402.

Digital Library

[34]

Xuanhui Wang, ChengXiang Zhai, Xiao Hu, and Richard Sproat. 2007. Mining correlated bursty topic patterns from coordinated text streams. In Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’07). 784--793.

Digital Library

[35]

Yu Wang, Eugene Agichtein, and Michele Benzi. 2012. TM-LDA: Efficient online modeling of latent topic transitions in social media. In Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’12). 123--131.

Digital Library

[36]

Z. Wang, L. Shou, K. Chen, G. Chen, and S. Mehrotra. 2015. On summarization and timeline generation for evolutionary tweet streams. IEEE Transactions on Knowledge and Data Engineering 27, 5 (2015), 1301--1315.

Digital Library

[37]

Andreas Weiler, Michael Grossniklaus, and Marc H. Scholl. 2017. Survey and experimental analysis of event detection techniques for Twitter. Computer Journal 60, 3 (2017), 329--346.

[38]

Jianshu Weng and Bu-Sung Lee. 2011. Event detection in Twitter. In Proceedings of the 5th International Conference on Weblogs and Social Media, Barcelona, Catalonia, Spain, July 17--21, 2011. 401--408.

[39]

Lexing Xie, Hari Sunaram, and Murray Campbell. 2007. Event mining in multimedia streams. Proceedings of the IEEE 96, 4 (2007), 623--647.

[40]

Wei Xie, Feida Zhu, Jing Jiang, Ee-Peng Lim, and Ke Wang. 2016. TopicSketch: Real-time bursty topic detection from Twitter. IEEE Transactions on Knowledge and Data Engineering 28, 8 (2016), 2216--2229.

Digital Library

[41]

Xiaohui Yan, Jiafeng Guo, Yanyan Lan, and Xueqi Cheng. 2013. A biterm topic model for short texts. In Proceedings of the 22nd International Conference on World Wide Web (WWW’13). ACM, New York, NY, 1445--1456.

Digital Library

[42]

Jaewon Yang and Jure Leskovec. 2011. Patterns of temporal variation in online media. In Proceedings of the 4th ACM International Conference on Web Search and Data Mining (WSDM’11). ACM, New York, NY, 177--186.

Digital Library

[43]

Yiming Yang, Tom Pierce, and Jaime Carbonell. 1998. A study of retrospective and on-line event detection. In Proceedings of the 21st ACM SIGIR (SIGIR’98). ACM, 28--36.

Digital Library

[44]

Junjie Yao, Bin Cui, Yuxin Huang, and Yanhong Zhou. 2012. Bursty event detection from collaborative tags. World Wide Web 15, 2 (2012), 171--195.

Digital Library

[45]

Jie Yin, Andrew Lampert, Mark A. Cameron, Bella Robinson, and Robert Power. 2012. Using social media to enhance emergency situation awareness. IEEE Intelligent Systems 27, 6 (2012), 52--59.

Digital Library

[46]

Yu Zhang and Zhiyi Qu. 2015. A novel method for online bursty event detection on Twitter. In Proceedings of the 6th IEEE International Conference on Software Engineering and Service Science (ICSESS’15). IEEE, 284--288.

[47]

Wayne Xin Zhao, Rishan Chen, Kai Fan, Hongfei Yan, and Xiaoming Li. 2012a. A novel burst-based text representation model for scalable event detection. In Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers -- Volume 2 (ACL’12). Association for Computational Linguistics, Stroudsburg, PA, 43--47.

Digital Library

[48]

Xin Zhao, Baihan Shu, Jing Jiang, Yang Song, Hongfei Yan, and Xiaoming Li. 2012b. Identifying event-related bursts via social media activities. In Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL’12). 1466--1477.

Digital Library

Cited By

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
Show More Cited By

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
        Blogs

Recommendations

Online Bursty Event Detection from Microblog
UCC '14: Proceedings of the 2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing

Microblogs (e.g., Twitter and Weibo) have become a large social media platform for users to share contents, their interests and events with friends. A surge of the number of event related posts always reflects that some people's concern real-life events ...
Event Detection on Curated Tweet Streams
SIGIR '17: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval

We present a system for identifying interesting social media posts on Twitter and delivering them to users' mobile devices in real time as push notifications. In our problem formulation, users are interested in broad topics such as politics, sports, and ...
Event detection using Twitter and structured semantic query expansion
CrowdSens '12: Proceedings of the 1st international workshop on Multimodal crowd sensing

Twitter is a popular tool for publishing potentially interesting information about people's opinions, experiences and news. Mobile devices allow people to publish tweets during real-time events. It is often difficult to identify the subject of a tweet ...

Comments

Information & Contributors

Information

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

Issue’s Table of Contents

Copyright © 2019 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]

Publisher

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

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

34
Total Citations
View Citations
917
Total Downloads

Downloads (Last 12 months)51
Downloads (Last 6 weeks)5

Reflects downloads up to 25 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

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
https://dl.acm.org/doi/10.1007/s00521-023-09050-6
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
https://doi.org/10.1109/CyberC58899.2023.00036
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
https://dl.acm.org/doi/10.1016/j.ins.2023.01.001
Liu YLuo XTao Z(2023)Construction of a high-precision general geographical location words datasetComputer Standards & Interfaces10.1016/j.csi.2022.10369284:COnline publication date: 1-Mar-2023
https://dl.acm.org/doi/10.1016/j.csi.2022.103692
Mredula MDey NRahman MMahmud ICho Y(2022)A Review on the Trends in Event Detection by Analyzing Social Media Platforms’ DataSensors10.3390/s2212453122:12(4531)Online publication date: 15-Jun-2022
https://doi.org/10.3390/s22124531
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Media

Figures

Other

Tables

View Issue’s Table of Contents