What to Predict from Twitter Data?

dc.authorscopusid57225195062en_US
dc.authorscopusid55779714800en_US
dc.contributor.authorSalemdeeb, Mohammed
dc.contributor.authorSahmoud, Shaaban
dc.contributor.authorSalem, Mohammed
dc.date.accessioned2025-05-17T10:17:47Z
dc.date.available2025-05-17T10:17:47Z
dc.date.issued2023en_US
dc.departmentMühendislik ve Doğa Bilimleri Fakültesien_US
dc.description.abstractIn the last decade, Twitter data has become one of the most valuable research sources for many areas including health, marketing, security, and politics. Researchers prefer Twitter data since it is completely public and can be easily downloaded using Twitter APIs. The recent intensive use of Twitter data makes it difficult for researchers to follow or analyze its research. In this paper, we summarize most of the predictable patterns, aspects, and attitudes from Twitter data and analyze the performance and feasibility of the algorithms used. Moreover, we describe the current popular Twitter datasets used in various domains and applications. Current challenges and research gaps are discussed, and some recommendations are given for future works from different perspectives.en_US
dc.identifier.citationSalemdeeb, M., & Sahmoud, S. (2023). What to predict from Twitter data? In 2023 3rd International Conference on Computing and Information Technology (ICCIT) (pp. 237–242). IEEE. https://doi.org/10.1109/ICCIT58132.2023.10273883en_US
dc.identifier.doi10.1109/ICCIT58132.2023.10273883
dc.identifier.endpage242en_US
dc.identifier.isbn979-835032148-7
dc.identifier.orcid0000-0002-2913-7671en_US
dc.identifier.scopus2-s2.0-85175464899en_US
dc.identifier.scopusqualityN/A
dc.identifier.startpage237en_US
dc.identifier.urihttps://doi.org/10.1109/ICCIT58132.2023.10273883
dc.identifier.urihttps://hdl.handle.net/20.500.12436/7675
dc.indekslendigikaynakScopus
dc.institutionauthorSalemdeeb, Mohammed
dc.language.isoen
dc.publisherIEEEen_US
dc.relation.ispartof2023 3rd International Conference on Computing and Information Technology, ICCIT 2023en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectPrediction from Twitteren_US
dc.subjectPredictive Analyticsen_US
dc.subjectTwitteren_US
dc.subjectTwitter data analysisen_US
dc.subjectTwitter datasetsen_US
dc.titleWhat to Predict from Twitter Data?en_US
dc.typeConference Object
dspace.entity.typePublication
relation.isAuthorOfPublication133c9305-961d-44a0-920a-c50351cc17fe
relation.isAuthorOfPublication.latestForDiscovery133c9305-961d-44a0-920a-c50351cc17fe

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