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المؤلفMasood, Faiza
المؤلفAmmad, Ghana
المؤلفAlmogren, Ahmad
المؤلفAbbas, Assad
المؤلفKhattak, Hasan Ali
المؤلفUd Din, Ikram
المؤلفGuizani, Mohsen
المؤلفZuair, Mansour
تاريخ الإتاحة2022-11-10T09:47:21Z
تاريخ النشر2019
اسم المنشورIEEE Access
المصدرScopus
المصدر2-s2.0-85067231140
معرّف المصادر الموحدhttp://dx.doi.org/10.1109/ACCESS.2019.2918196
معرّف المصادر الموحدhttp://hdl.handle.net/10576/36118
الملخصSocial networking sites engage millions of users around the world. The users' interactions with these social sites, such as Twitter and Facebook have a tremendous impact and occasionally undesirable repercussions for daily life. The prominent social networking sites have turned into a target platform for the spammers to disperse a huge amount of irrelevant and deleterious information. Twitter, for example, has become one of the most extravagantly used platforms of all times and therefore allows an unreasonable amount of spam. Fake users send undesired tweets to users to promote services or websites that not only affect legitimate users but also disrupt resource consumption. Moreover, the possibility of expanding invalid information to users through fake identities has increased that results in the unrolling of harmful content. Recently, the detection of spammers and identification of fake users on Twitter has become a common area of research in contemporary online social Networks (OSNs). In this paper, we perform a review of techniques used for detecting spammers on Twitter. Moreover, a taxonomy of the Twitter spam detection approaches is presented that classifies the techniques based on their ability to detect: (i) fake content, (ii) spam based on URL, (iii) spam in trending topics, and (iv) fake users. The presented techniques are also compared based on various features, such as user features, content features, graph features, structure features, and time features. We are hopeful that the presented study will be a useful resource for researchers to find the highlights of recent developments in Twitter spam detection on a single platform. 2013 IEEE.
راعي المشروعThe authors are grateful to the Deanship of Scientific Research, King Saud University for funding through the Vice Deanship of Scientific Research Chair.
اللغةen
الناشرInstitute of Electrical and Electronics Engineers Inc.
الموضوعClassification
fake user detection
online social network
spammer's identification
العنوانSpammer Detection and Fake User Identification on Social Networks
النوعArticle
الصفحات68140-68152
رقم المجلد7
dc.accessType Open Access


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