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Author
Federico Angaramo
;
Claudio Rossi
Title
Online clustering and classification for real-time event detection in Twitter
Type
Conference Article
Year
2018
Publication
ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management
Abbreviated Journal
Iscram 2018
Volume
Issue
Pages
1098-1107
Keywords
Event detection, Social Media, Clustering, Machine Learning, Twitter
Abstract
Event detection from social media is a challenging task due to the volume, the velocity and the variety of user-generated data requiring real-time processing. Despite recent works on this subject, a generalized and scalable approach that could be applied across languages and topics has not been consolidated, yet. In this paper, we propose a methodology for real-time event detection from Twitter data that allows users to select a topic of interest by defining a simple set of keywords and a matching rule. We implement the proposed methodology and evaluate it with real data to detect different types of events.
Address
Corporate Author
Thesis
Publisher
Rochester Institute of Technology
Place of Publication
Rochester, NY (USA)
Editor
Kees Boersma; Brian Tomaszeski
Language
English
Summary Language
English
Original Title
Series Editor
Series Title
Abbreviated Series Title
Series Volume
Series Issue
Edition
ISSN
2411-3387
ISBN
978-0-692-12760-5
Medium
Track
1st International Workshop on Intelligent Crisis Management Technologies for Climate Events (ICMT)
Expedition
Conference
ISCRAM 2018 Conference Proceedings - 15th International Conference on Information Systems for Crisis Response and Management
Notes
Approved
no
Call Number
Serial
2182
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