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Paper Title : Spammer Detection and Fake User Identification on social media
ISSN : 2395-1303
Year of Publication : 2022
MLA Style: -B. Prathyusha, B. Saisree, E. Nikshitha, D. Shivalini Spammer Detection and Fake User Identification on social media , Volume 8 - Issue 5 September - October 2022 International Journal of Engineering and Techniques (IJET) ,ISSN:2395-1303 , www.ijetjournal.org
APA Style: -B. Prathyusha, B. Saisree, E. Nikshitha, D. Shivalini Spammer Detection and Fake User Identification on social media , Volume 8 - Issue 5 September - October 2022 International Journal of Engineering and Techniques (IJET) ,ISSN:2395-1303 , www.ijetjournal.org
Social networking sites have interaction immeasurable users round the world. The users’ interactions with these social sites, like Twitter and Facebook have an amazing impact and infrequently undesirable repercussions for everyday life. The outstanding social networking sites have become a target platform for the spammers to disperse an enormous quantity of digressive and injurious data. Twitter, for instance, has become one in every of the foremost extravagantly used platforms of all times and thus permits associate unreasonable quantity of spam. faux users send unwanted tweets to users to market services or websites that not solely influence legitimate users however additionally disrupt resource consumption. Moreover, the chance of increasing invalid data to users through faux identities has inflated that ends up in the unrolling of harmful content. Recently, the detection of spammers and identification of pretend users on Twitter has become a standard space of analysis in up to date on-line social Networks (OSNs). during this paper, we tend to perform a review of techniques used for police investigation spammers on Twitter. Moreover, a taxonomy of the Twitter spam detection approaches is given that classifies the techniques supported their ability to detect: (i) faux content, (ii) spam supported computer address, (iii) spam in trending topics, and (iv) faux users. The given techniques also are compared supported varied options, like user options, content options, graph options, structure options, and time options. we tend to hopeful that the given study is a helpful resource for researchers to search out the highlights of recent developments in Twitter spam detection on one platform.
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— Classification, fake user detection, online social network, spammer’s identification.