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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

10938

Series

Lecture Notes in Computer Science

Pagination

534 - 545

Publisher

Springer

Location

Melbourne, Australia

Place of publication

Cham, Switzerland

Start date

2018-06-03

End date

2018-06-06

ISSN

0302-9743

eISSN

1611-3349

ISBN-13

9783319930367

Language

eng

Publication classification

E1.1 Full written paper - refereed

Editor/Contributor(s)

D Phung, V Tseng, G Webb, B Ho, M Ganji, L Rashidi

Title of proceedings

PAKDD 2018: Advances in Knowledge Discovery and Data Mining : Pacific-Asia Conference on Knowledge Discovery and Data Mining

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