Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11889/6787
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dc.contributor.authorAbuhammad, Yaraen_US
dc.contributor.authorAddabe, Yaraen_US
dc.contributor.authorAyyad, Natalyen_US
dc.contributor.authorYahya, Adnanen_US
dc.date.accessioned2021-09-01T05:55:33Z-
dc.date.available2021-09-01T05:55:33Z-
dc.date.issued2021-08-22-
dc.identifier.citationAbuhammad Y., Aldabe Y., Ayyad N. and Yahya, A. "Authorship Attribution of Modern Standard Arabic Short Texts". Proceedings of the Seventh International Conference on Arab Women in Computing ArabWIC21. Sharjah. United Arab Emirates. 25 August 2021.ACM proceedings.en_US
dc.identifier.urihttp://hdl.handle.net/20.500.11889/6787-
dc.descriptionArabWIC 2021: ArabWIC 7th International Conference on Arab Women in Computing In Conjunction with 2nd Forum for Women in Research As Part of the ArabWIC 2021 Conference Series August 25, 2021en_US
dc.description.abstractText data, including short texts, constitute a major share of web content. The availability of this data to billions of users triggers frequent plagiarism attacks. Authorship Attribution (AA) seeks to identify the most probable author of a given text based on similarity to the writing style of potential authors. In this paper, we approach AA as a writing style profile generation process, where we group text instances for each author into a single profile. We use Twitter as the source for our short Modern Standard Arabic (MSA) texts. Numerous experiments with various training approaches, tools and features allowed us to settle on a text representation method that relies on text concatenation of Arabic tweets to form chunks, which are then duplicated to reach a precalculated length. These chunks are used to train machine learning models for our 45 author profiles. This allowed us to achieve accuracies up to 99%, which compares favorably with the best results reported in the literature.en_US
dc.language.isoenen_US
dc.publisherAssociation for Computing Machinery-ACMen_US
dc.subjectArabic authorship attributionen_US
dc.subjectSupport vector classificationen_US
dc.subjectMachine learning - Techniqueen_US
dc.subjectSocial mediaen_US
dc.subjectTwitteren_US
dc.subjectAuthorship attribution datasetsen_US
dc.subjectText processing (Computer science), Arabicen_US
dc.subjectNatural language processing (Computer science) - Arab countriesen_US
dc.subjectTweets, Arabicen_US
dc.subjectComputational linguistics, Arabicen_US
dc.titleAuthorship Attribution of Modern Standard Arabic Short Textsen_US
dc.typeArticleen_US
dcterms.creatorAdnan Yahyaen_US
dcterms.formatpdfen_US
newfileds.departmentEngineering and Technologyen_US
newfileds.item-access-typebzuen_US
newfileds.thesis-prognoneen_US
newfileds.general-subjectComputers and Information Technology | الحاسوب وتكنولوجيا المعلوماتen_US
dc.relation.conferenceArabWIC 2021: ArabWIC 7th International Conference on Arab Women in Computing.en_US
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item.languageiso639-1other-
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