Research application of the spam filtering and spammer detection algorithms on social media

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dc.contributor.authorOliinyk, V.
dc.date.accessioned2022-11-04T12:42:42Z
dc.date.available2022-11-04T12:42:42Z
dc.date.issued2022
dc.descriptionOliinyk V. Research application of the spam filtering and spammer detection algorithms on social media / V. Oliinyk ; advisors A. Podorozhniak, N. Liubchenko // Black Sea Science 2022 : proc. of the Intern. Competition of Student Scientific Works / Odesa National University of Technology ; eds. B. Yegorov, M. Mardar [et al.]. – Odessa : ONUT, 2022. – P. 480–494 : tabl., fig. – Ref.: 22 tit.uk_UA
dc.description.abstractThere are a bunch of different social networks and messengers today, which in times of pandemic corona-virus have take a really big part of our entire live, especially in our work activities. Besides that, the problem with the spam and spammers is the most relevant than ever, the count of spam in the work text stream is continuously increased. Under spam we understand the text content that is not necessary in the particular text stream, in case of spammer it is meant the person that is sending the spam messages in his or her own purposes. The project was design to solve the scientific and applied problem of detecting spammers and identifying spam messages in the text context of any social network or messenger using various spam detection algorithms and spammer detection approaches. We have implemented 4 algorithms: an algorithm using naive Bayesian classifier, Support-vector machine, multilayer perceptron neural network and convolution neural network. The project was developed in purpose of implementing a spam detection algorithm that is easy to integrate in a messenger (in our case we used Telegram as an example). Design algorithm recognizes spam based on the context of a particular text stream, deletes the spam message and blocks the spammer until one of the application managers unblock the spammer-user. Since the spam detection task is essentially the task of sorting messages into two classes, the usage of the design application is not limited to dealing with spam.uk_UA
dc.identifier.urihttps://card-file.ontu.edu.ua/handle/123456789/23791
dc.subjectspamuk_UA
dc.subjectsocial networkuk_UA
dc.subjectnaive Bayesian classifieruk_UA
dc.subjectSupport-vector machineuk_UA
dc.subjectmultilayer perceptron neural networkuk_UA
dc.subjectconvolution neural networkuk_UA
dc.subjectspammers detectionuk_UA
dc.titleResearch application of the spam filtering and spammer detection algorithms on social mediauk_UA
dc.typeArticleuk_UA
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