Context-dependent model for spam detection on social networks

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dc.contributor.authorGhanem, Razan
dc.contributor.authorErbay, Hasan
dc.date.accessioned2021-01-14T18:10:25Z
dc.date.available2021-01-14T18:10:25Z
dc.date.issued2020
dc.departmentKKÜ
dc.description.abstractSocial media platforms are getting an important communication medium in our daily life, and their increasing popularity makes them an ideal platform for spammers to spread spam messages, known as spam problems. Moreover, messages on social media are vague and messy, so a good representation of the text may be the first step to address spam problem. While traditional weighting methods suffer from both high dimensionality and high sparsity problems, traditional word embedding methods suffer from context independence and out of vocabulary problems. To overcome these problems, in this study, we propose a novel architecture based on a context-dependent representation of text using the BERT model. The model was tested using the Twitter dataset, and experimental results show that the proposed method outperforms traditional weighting methods, traditional word embedding based methods as well as the existing state of the art methods used to detect spam on the twitter platform.en_US
dc.identifier.citationGhanem, R., & Erbay, H. (2020). Context-dependent model for spam detection on social networks. SN Applied Sciences, 2(9), 1-8.en_US
dc.identifier.doi10.1007/s42452-020-03374-x
dc.identifier.issn2523-3963
dc.identifier.issn2523-3971
dc.identifier.issue9en_US
dc.identifier.scopus2-s2.0-85100788447
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.1007/s42452-020-03374-x
dc.identifier.urihttps://hdl.handle.net/20.500.12587/12591
dc.identifier.volume2en_US
dc.identifier.wosWOS:000563838000004
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSPRINGER INTERNATIONAL PUBLISHING AGen_US
dc.relation.ispartofSN APPLIED SCIENCES
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectSpam detectionen_US
dc.subjectWord embeddingen_US
dc.subjectBidirectional encoder representations from transformersen_US
dc.titleContext-dependent model for spam detection on social networksen_US
dc.typeArticle

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