Text stream to temporal network - A dynamic heartbeat graph to detect emerging events on twitter

Saeed, Z; Abbasi, RA; Sadaf, A; Razzak, MI; Xu, G

Text stream to temporal network - A dynamic heartbeat graph to detect emerging events on twitter

Saeed, Z Abbasi, RA Sadaf, A Razzak, MI

Xu, G

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2018, 10938 LNAI pp. 534 - 545
Issue Date:: 2018-01-01

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Field	Value	Language
dc.contributor.author	Saeed, Z	en_US
dc.contributor.author	Abbasi, RA	en_US
dc.contributor.author	Sadaf, A	en_US
dc.contributor.author	Razzak, MI https://orcid.org/0000-0002-3930-6600	en_US
dc.contributor.author	Xu, G https://orcid.org/0000-0003-4493-6663	en_US
dc.date.issued	2018-01-01	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2018, 10938 LNAI pp. 534 - 545	en_US
dc.identifier.isbn	9783319930367	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/126038
dc.description.abstract	© 2018, Springer International Publishing AG, part of Springer Nature. Huge mounds of data are generated every second on the Internet. People around the globe publish and share information related to real-world events they experience every day. This provides a valuable opportunity to analyze the content of this information to detect real-world happenings, however, it is quite challenging task. In this work, we propose a novel graph-based approach named the Dynamic Heartbeat Graph (DHG) that not only detects the events at an early stage, but also suppresses them in the upcoming adjacent data stream in order to highlight new emerging events. This characteristic makes the proposed method interesting and efficient in finding emerging events and related topics. The experiment results on real-world datasets (i.e. FA Cup Final and Super Tuesday 2012) show a considerable improvement in most cases, while time complexity remains very attractive.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.relation.isbasedon	10.1007/978-3-319-93037-4_42	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Text stream to temporal network - A dynamic heartbeat graph to detect emerging events on twitter	en_US
dc.type	Conference Proceeding
utslib.citation.volume	10938 LNAI	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Software
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US
pubs.volume	10938 LNAI	en_US

Abstract:

© 2018, Springer International Publishing AG, part of Springer Nature. Huge mounds of data are generated every second on the Internet. People around the globe publish and share information related to real-world events they experience every day. This provides a valuable opportunity to analyze the content of this information to detect real-world happenings, however, it is quite challenging task. In this work, we propose a novel graph-based approach named the Dynamic Heartbeat Graph (DHG) that not only detects the events at an early stage, but also suppresses them in the upcoming adjacent data stream in order to highlight new emerging events. This characteristic makes the proposed method interesting and efficient in finding emerging events and related topics. The experiment results on real-world datasets (i.e. FA Cup Final and Super Tuesday 2012) show a considerable improvement in most cases, while time complexity remains very attractive.

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http://hdl.handle.net/10453/126038