Publication: Analyzing Algorithms to Detect Disaster Events using Social Media
Date
2020
Authors
Azlan F.A.
Ahmad A.
Yussof S.
Ghapar A.A.
Journal Title
Journal ISSN
Volume Title
Publisher
Institute of Electrical and Electronics Engineers Inc.
Abstract
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.
Description
Nearest neighbor search; Social networking (online); Support vector machines; Categorization systems; K nearest neighbor (KNN); Naive bayes; Social media; Three models; Disasters