What to Predict from Twitter Data?

dc.authorscopusid57225195062
dc.authorscopusid55779714800
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.issued2023
dc.departmentMühendislik ve Doğa Bilimleri Fakültesi
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.
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/ICCIT58132.2023.10273883
dc.identifier.doi10.1109/ICCIT58132.2023.10273883
dc.identifier.endpage242
dc.identifier.isbn979-835032148-7
dc.identifier.orcid0000-0002-2913-7671
dc.identifier.scopus2-s2.0-85175464899
dc.identifier.scopusqualityN/A
dc.identifier.startpage237
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.publisherIEEE
dc.relation.ispartof2023 3rd International Conference on Computing and Information Technology, ICCIT 2023
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectPrediction from Twitter
dc.subjectPredictive Analytics
dc.subjectTwitter
dc.subjectTwitter data analysis
dc.subjectTwitter datasets
dc.titleWhat to Predict from Twitter Data?
dc.typeConference Object
dspace.entity.typePublication
relation.isAuthorOfPublication133c9305-961d-44a0-920a-c50351cc17fe
relation.isAuthorOfPublication.latestForDiscovery133c9305-961d-44a0-920a-c50351cc17fe

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