File(s) under permanent embargo
Text stream to temporal network - a dynamic heartbeat graph to detect emerging events on twitter
conference contribution
posted on 2018-01-01, 00:00 authored by Z Saeed, R A Abbasi, A Sadaf, Imran RazzakImran Razzak, G Xu© 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.
History
Event
Knowledge Discovery and Data Mining. Pacific-Asia Conference (2018 : Melbourne, Australia)Volume
10938Series
Lecture Notes in Computer SciencePagination
534 - 545Publisher
SpringerLocation
Melbourne, AustraliaPlace of publication
Cham, SwitzerlandPublisher DOI
Start date
2018-06-03End date
2018-06-06ISSN
0302-9743eISSN
1611-3349ISBN-13
9783319930367Language
engPublication classification
E1.1 Full written paper - refereedEditor/Contributor(s)
D Phung, V Tseng, G Webb, B Ho, M Ganji, L RashidiTitle of proceedings
PAKDD 2018: Advances in Knowledge Discovery and Data Mining : Pacific-Asia Conference on Knowledge Discovery and Data MiningUsage metrics
Categories
No categories selectedKeywords
Licence
Exports
RefWorks
BibTeX
Ref. manager
Endnote
DataCite
NLM
DC