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Review article: Detection of informative tweets in crisis events

Kruspe, Anna and Kersten, Jens and Klan, Friederike (2021) Review article: Detection of informative tweets in crisis events. Natural Hazards and Earth System Sciences (NHESS). Copernicus Publications. doi: 10.5194/nhess-2020-214. ISSN 1561-8633. (Submitted)

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Official URL: https://nhess.copernicus.org/preprints/nhess-2020-214/

Abstract

One challenge for utilizing social media in crisis situations is the reliable detection of informative messages in a flood of data. Researchers have started to look into this problem in recent years, beginning with crowd-sourced methods. Lately, approaches have shifted towards an automatic analysis of messages. In this review article, we present methods for the automatic detection of crisis-related messages (tweets) on Twitter. We start by showing the varying definitions of importance and relevance relating to disasters, as they can serve very different purposes. This is followed by an overview of existing, crisis-related social media data sets for evaluation and training purposes. We then compare approaches for solving the detection problem based (1) on filtering by characteristics like keywords and location, (2) on crowdsourcing, and (3) on machine learning techniques with regard to their focus, their data requirements, their technical prerequisites, their efficiency and accuracy, and their time scales. These factors determine the suitability of the approaches for different expectations, but also their limitations. We identify which aspects each of them can contribute to the detection of informative tweets, and which areas can be improved upon in the future.We point out particular challenges, such as the linguistic issues concerning this kind of data. Finally, we suggest future avenues of research, and show connections to related tasks, such as the subsequent semantic classification of tweets.

Item URL in elib:https://elib.dlr.de/140118/
Document Type:Article
Title:Review article: Detection of informative tweets in crisis events
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Kruspe, Annaanna.kruspe (at) dlr.dehttps://orcid.org/0000-0002-2041-9453
Kersten, Jensjens.kersten (at) dlr.dehttps://orcid.org/0000-0002-4735-7360
Klan, FriederikeFriederike.Klan (at) dlr.dehttps://orcid.org/0000-0002-1856-7334
Date:2021
Journal or Publication Title:Natural Hazards and Earth System Sciences (NHESS)
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In SCOPUS:Yes
In ISI Web of Science:Yes
DOI :10.5194/nhess-2020-214
Editors:
EditorsEmailEditor's ORCID iD
Figueiredo, RuiUNSPECIFIEDUNSPECIFIED
Schröter, KaiUNSPECIFIEDUNSPECIFIED
Martina, Mario Lloyd Virgiliomario.martina@iusspavia.itUNSPECIFIED
Galasso, CarmineUNSPECIFIEDUNSPECIFIED
Publisher:Copernicus Publications
Series Name:Special Issue: Groundbreaking technologies, big data, and innovation for disaster risk modelling and reduction
ISSN:1561-8633
Status:Submitted
Keywords:Twitter, crisis informatics, crisis relevance, machine learning
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:07 Jan 2021 10:22
Last Modified:07 Jan 2021 10:22

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