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Analysis of Detection Models for Disaster-Related Tweets

Wiegmann, Matti and Kersten, Jens and Klan, Friederike and Potthast, Martin and Stein, Benno (2020) Analysis of Detection Models for Disaster-Related Tweets. In: Analysis of Detection Models for Disaster-Related Tweets, pp. 872-880. ISCRAM 2020, scheduled May 2020, postponed due to Covid-19, Blacksburg, VA, USA. ISBN 2411-3463. ISSN 978-1-949373-27-77.

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Official URL: http://idl.iscram.org/files/mattiwiegmann/2020/2278_MattiWiegmann_etal2020.pdf

Abstract

Social media is perceived as a rich resource for disaster management and relief efforts, but the high class imbalance between disaster-related and non-disaster-related messages challenges a reliable detection. We analyze and compare the effectiveness of three state-of-the-art machine learning models for detecting disaster-related tweets. In this regard we introduce the Disaster Tweet Corpus 2020, an extended compilation of existing resources, which comprises a total of 123,166 tweets from 46 disasters covering 9 disaster types. Our findings from a large experiments series include: detection models work equally well over a broad range of disaster types when being trained for the respective type, a domain transfer across disaster types leads to unacceptable performance drops, or, similarly, type-agnostic classification models behave more robust at a lower effectiveness level. Altogether, the average misclassification rate of 3,8\% on performance-optimized detection models indicates effective classification knowledge but comes at the price of insufficient generalizability.

Item URL in elib:https://elib.dlr.de/137213/
Document Type:Conference or Workshop Item (Speech)
Title:Analysis of Detection Models for Disaster-Related Tweets
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Wiegmann, MattiMatti.Wiegmann (at) dlr.deUNSPECIFIED
Kersten, Jensjens.kersten (at) dlr.dehttps://orcid.org/0000-0002-4735-7360
Klan, FriederikeFriederike.Klan (at) dlr.dehttps://orcid.org/0000-0002-1856-7334
Potthast, MartinLeipzig UniversityUNSPECIFIED
Stein, BennoBauhaus-Universität WeimarUNSPECIFIED
Date:May 2020
Journal or Publication Title:Analysis of Detection Models for Disaster-Related Tweets
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Page Range:pp. 872-880
Editors:
EditorsEmailEditor's ORCID iD
Hughes, Amanda LeeUNSPECIFIEDUNSPECIFIED
McNeill, FionaUNSPECIFIEDUNSPECIFIED
Zobel, ChristopherUNSPECIFIEDUNSPECIFIED
ISSN:978-1-949373-27-77
ISBN:2411-3463
Status:Published
Keywords:Tweet Filtering, Crisis Management, Evaluation Framework
Event Title:ISCRAM 2020
Event Location:Blacksburg, VA, USA
Event Type:international Conference
Event Dates:scheduled May 2020, postponed due to Covid-19
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:other
DLR - Research area:Raumfahrt
DLR - Program:R - no assignment
DLR - Research theme (Project):R - no assignment
Location: Jena
Institutes and Institutions:Institute of Data Science > Citizen Science
Deposited By: Kersten, Dr.-Ing. Jens
Deposited On:13 Nov 2020 14:01
Last Modified:13 Nov 2020 14:01

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