Selecting classification features for detection of mass emergencies on social media

Viktor Pekar, Jane Binner, Hossein Najafi, Christopher Hale

Research output: Chapter in Book/Published conference outputConference publication

Abstract

The paper addresses the problem of detecting eyewitness reports of mass emergencies on Twitter. This is the first work to conduct a large-scale comparative evaluation of classification features extracted from Twitter posts, using different learning algorithms and datasets representing a broad range of mass emergencies including both natural and technological disasters. We investigate the relative importance of different feature types as well as on the effect of several feature selection methods applied to this problem. Because the task of detecting mass emergencies is characterized by high heterogeneity of the data, our primary focus is on identifying those features that are capable of separating mass emergency reports from other messages, irrespective of the type of the disaster.
Original languageEnglish
Title of host publicationProceedings of the 2016 International Conference on Security and Management
PublisherCSREA
Pages192-198
Number of pages7
ISBN (Print)9781601324450, 1-60132-445-6
Publication statusPublished - 13 Apr 2017
Event2016 International Conference on Security and Management (SAM'16)
- Las Vegas, United States
Duration: 25 Jul 201628 Jul 2016

Conference

Conference2016 International Conference on Security and Management (SAM'16)
CountryUnited States
CityLas Vegas
Period25/07/1628/07/16

Bibliographical note

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