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Analyzing Algorithms to Detect Disaster Events using Social Media

Nearest neighbor search; Social networking (online); Support vector machines; Categorization systems; K nearest neighbor (KNN); Naive bayes; Social media; Three models; Disasters

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Bibliographic Details
Main Authors: Azlan F.A., Ahmad A., Yussof S., Ghapar A.A.
Other Authors: 57220804102
Format: Conference Paper
Published: Institute of Electrical and Electronics Engineers Inc. 2023
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id my.uniten.dspace-25317
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spelling my.uniten.dspace-253172023-05-29T16:08:06Z Analyzing Algorithms to Detect Disaster Events using Social Media Azlan F.A. Ahmad A. Yussof S. Ghapar A.A. 57220804102 55390963300 16023225600 35172922200 Nearest neighbor search; Social networking (online); Support vector machines; Categorization systems; K nearest neighbor (KNN); Naive bayes; Social media; Three models; Disasters Disasters are instabilities that occur on the interface between society and the environment. During disasters, people communicate to inform and request for support for themselves or their community. Social media is used as a medium for communication due to its wide reach and global audience. During disasters, people communicate via messages regarding similar or different types of emergencies in the same general location. Interpreting and validating these messages during the occurrence of a disaster costs a significant time and loss. Therefore, this study presents a comparison between three models, K-Nearest Neighbor (KNN), Naive Bayes (NB), and Support Vector Machine (SVM), to classify and label a message as a disaster event. In order to simulate the examining process further, a categorization system is introduced to categorize the severity of a disaster as described in each message in a disaster environment. performances are compared for each of the models using classification scores of supervised learning. � 2020 IEEE. Final 2023-05-29T08:08:06Z 2023-05-29T08:08:06Z 2020 Conference Paper 10.1109/ICIMU49871.2020.9243599 2-s2.0-85097647580 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097647580&doi=10.1109%2fICIMU49871.2020.9243599&partnerID=40&md5=f5d597c0e600f8a26226659f525a9ed4 https://irepository.uniten.edu.my/handle/123456789/25317 9243599 384 389 Institute of Electrical and Electronics Engineers Inc. Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description Nearest neighbor search; Social networking (online); Support vector machines; Categorization systems; K nearest neighbor (KNN); Naive bayes; Social media; Three models; Disasters
author2 57220804102
author_facet 57220804102
Azlan F.A.
Ahmad A.
Yussof S.
Ghapar A.A.
format Conference Paper
author Azlan F.A.
Ahmad A.
Yussof S.
Ghapar A.A.
spellingShingle Azlan F.A.
Ahmad A.
Yussof S.
Ghapar A.A.
Analyzing Algorithms to Detect Disaster Events using Social Media
author_sort Azlan F.A.
title Analyzing Algorithms to Detect Disaster Events using Social Media
title_short Analyzing Algorithms to Detect Disaster Events using Social Media
title_full Analyzing Algorithms to Detect Disaster Events using Social Media
title_fullStr Analyzing Algorithms to Detect Disaster Events using Social Media
title_full_unstemmed Analyzing Algorithms to Detect Disaster Events using Social Media
title_sort analyzing algorithms to detect disaster events using social media
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2023
_version_ 1806424455176519680
score 13.222552

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