Detecting spam tweets using machine learning and effective preprocessing

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dc.contributor.authorKardaş, Berk
dc.contributor.authorBayar, İsmail Erdem
dc.contributor.authorÖzyer, Tansel
dc.contributor.authorAlhajj, Reda
dc.date.accessioned2022-02-28T11:24:19Z
dc.date.available2022-02-28T11:24:19Z
dc.date.issued2021
dc.departmentİstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractNowadays, with the rapid increase in popularity of online social networks (OSNs), these platforms are realized as ideal places for spammers. Unfortunately, these spammers can easily publish malicious content, advertise phishing scams by taking advantage of OSNs. Therefore, effective identification and filtering of spam tweets will be beneficial to both OSNs and users. However, it is becoming increasingly difficult to check and eliminate spam tweets due to this great flow of posts. Motivated by these observations, in this paper we propose an approach for the detection of spam tweets using machine learning and effective preprocessing techniques. The approach proposes the advantages of the preprocessing and which of these preprocessing techniques are the most effective. To compare these techniques UtkML Twitter spam dataset is used in testing. After the most effective methods determined, the detection accuracy of the spam tweets will be better optimized by combining them. We have evaluated our solution with four different machine learning algorithms namely - Naïve Bayes Classifier, Neural Network, Logistic Regression and Support Vector Machine. With SVM Classifier, we are able to achieve an accuracy of 93.02%. Experimental results show that our approach can improve the performance of spam tweet classification effectively.
dc.description.sponsorshipACM Special Interest Group on Knowledge Discovery in Data (SIGKDD) ; Elsevier ; IEEE Computer Society ; IEEE TCDE ; Springeren_US
dc.identifier.citationKardaş, B., Bayar, İ. E., Özyer, T. ve Alhajj, R. (2021). Detecting spam tweets using machine learning and effective preprocessing. 13th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM içinde (393-398. ss.). Virtual, Online, 8 November 2021. https://doi.org/10.1145/3487351.3490968
dc.identifier.doi10.1145/3487351.3490968
dc.identifier.endpage398
dc.identifier.isbn9781450391283
dc.identifier.scopus2-s2.0-85124395764
dc.identifier.scopusqualityN/A
dc.identifier.startpage393
dc.identifier.urihttps://doi.org/10.1145/3487351.3490968
dc.identifier.urihttps://hdl.handle.net/20.500.12511/9025
dc.indekslendigikaynakScopus
dc.institutionauthorÖzyer, Tansel
dc.language.isoen
dc.publisherAssociation for Computing Machinery, Inc
dc.relation.ispartof13th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAMen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectMachine Learning
dc.subjectPreprocessing
dc.subjectSocial Media
dc.subjectSpam Detection
dc.subjectTwitter
dc.titleDetecting spam tweets using machine learning and effective preprocessing
dc.typeConference Object

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