A state-of-art optimization method for analyzing the tweets of earthquake-prone region

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dc.authoridEliguzel, Nazmiye/0000-0001-6354-8215
dc.authoridCetinkaya, Cihan/0000-0002-5899-8438
dc.authoridDereli, Turkay/0000-0002-2130-5503
dc.contributor.authorEliguzel, Nazmiye
dc.contributor.authorCetinkaya, Cihan
dc.contributor.authorDereli, Turkay
dc.date.accessioned2025-01-06T17:36:59Z
dc.date.available2025-01-06T17:36:59Z
dc.date.issued2021
dc.description.abstractWith the increase in accumulated data and usage of the Internet, social media such as Twitter has become a fundamental tool to access all kinds of information. Therefore, it can be expressed that processing, preparing data, and eliminating unnecessary information on Twitter gains its importance rapidly. In particular, it is very important to analyze the information and make it available in emergencies such as disasters. In the proposed study, an earthquake with the magnitude of Mw = 6.8 on the Richter scale that occurred on January 24, 2020, in Elazig province, Turkey, is analyzed in detail. Tweets under twelve hashtags are clustered separately by utilizing the Social Spider Optimization (SSO) algorithm with some modifications. The sum-of intra-cluster distances (SICD) is utilized to measure the performance of the proposed clustering algorithm. In addition, SICD, which works in a way of assigning a new solution to its nearest node, is used as an integer programming model to be solved with the GUROBI package program on the test data-sets. Optimal results are gathered and compared with the proposed SSO results. In the study, center tweets with optimal results are found by utilizing modified SSO. Moreover, results of the proposed SSO algorithm are compared with the K-means clustering technique which is the most popular clustering technique. The proposed SSO algorithm gives better results. Hereby, the general situation of society after an earthquake is deduced to provide moral and material supports.
dc.identifier.doi10.1007/s00521-021-06109-0
dc.identifier.endpage14705
dc.identifier.issn0941-0643
dc.identifier.issn1433-3058
dc.identifier.issue21
dc.identifier.scopus2-s2.0-85106414410
dc.identifier.scopusqualityQ1
dc.identifier.startpage14687
dc.identifier.urihttps://doi.org/10.1007/s00521-021-06109-0
dc.identifier.urihttps://hdl.handle.net/20.500.14669/2071
dc.identifier.volume33
dc.identifier.wosWOS:000653636500001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer London Ltd
dc.relation.ispartofNeural Computing & Applications
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241211
dc.subjectClustering
dc.subjectLatent semantic analyses
dc.subjectSocial spider optimization
dc.subjectTwitter
dc.titleA state-of-art optimization method for analyzing the tweets of earthquake-prone region
dc.typeArticle

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