Publication:
Analyzing Algorithms to Detect Disaster Events using Social Media

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dc.citedby1
dc.contributor.authorAzlan F.A.en_US
dc.contributor.authorAhmad A.en_US
dc.contributor.authorYussof S.en_US
dc.contributor.authorGhapar A.A.en_US
dc.contributor.authorid57220804102en_US
dc.contributor.authorid55390963300en_US
dc.contributor.authorid16023225600en_US
dc.contributor.authorid35172922200en_US
dc.date.accessioned2023-05-29T08:08:06Z
dc.date.available2023-05-29T08:08:06Z
dc.date.issued2020
dc.descriptionNearest neighbor search; Social networking (online); Support vector machines; Categorization systems; K nearest neighbor (KNN); Naive bayes; Social media; Three models; Disastersen_US
dc.description.abstractDisasters 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.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo9243599
dc.identifier.doi10.1109/ICIMU49871.2020.9243599
dc.identifier.epage389
dc.identifier.scopus2-s2.0-85097647580
dc.identifier.spage384
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85097647580&doi=10.1109%2fICIMU49871.2020.9243599&partnerID=40&md5=f5d597c0e600f8a26226659f525a9ed4
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25317
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitle2020 8th International Conference on Information Technology and Multimedia, ICIMU 2020
dc.titleAnalyzing Algorithms to Detect Disaster Events using Social Mediaen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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