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AuthorMasood, Faiza
AuthorAmmad, Ghana
AuthorAlmogren, Ahmad
AuthorAbbas, Assad
AuthorKhattak, Hasan Ali
AuthorUd Din, Ikram
AuthorGuizani, Mohsen
AuthorZuair, Mansour
Available date2022-11-10T09:47:21Z
Publication Date2019
Publication NameIEEE Access
ResourceScopus
Resource2-s2.0-85067231140
URIhttp://dx.doi.org/10.1109/ACCESS.2019.2918196
URIhttp://hdl.handle.net/10576/36118
AbstractSocial 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.
SponsorThe authors are grateful to the Deanship of Scientific Research, King Saud University for funding through the Vice Deanship of Scientific Research Chair.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectClassification
fake user detection
online social network
spammer's identification
TitleSpammer Detection and Fake User Identification on Social Networks
TypeArticle
Pagination68140-68152
Volume Number7
dc.accessType Open Access


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