Spammer Detection and Fake User Identificationon Social Networks
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The document discusses the rampant issue of spam and fake users on social networking sites, particularly Twitter, highlighting the adverse effects of irrelevant information and harmful content. It reviews various techniques for spam detection and categorizes them based on their capabilities to identify fake content, URLs, trending topic spam, and fraudulent users. The paper aims to serve as a comprehensive resource for researchers focusing on advancements in Twitter spam detection methodologies.
Spammer Detection and Fake User Identificationon Social Networks
- 1. Spammer Detection and Fake User Identification on Social Networks ABSTRACT: Social 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
- 2. 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. SYSTEM REQUIREMENTS: HARDWARE REQUIREMENTS: System : Pentium Dual Core. Hard Disk : 120 GB. Monitor : 15’’ LED Input Devices : Keyboard, Mouse Ram : 1 GB. SOFTWARE REQUIREMENTS: Operating system : Windows 7. Coding Language : ASP.NET,C#.NET Tool : Visual Studio 2008 Database : SQL SERVER 2005 REFERENCE: FAIZA MASOOD1, GHANA AMMAD1, AHMAD ALMOGREN 2, (Senior Member, IEEE), ASSAD ABBAS 1, HASAN ALI KHATTAK 1, (Senior Member, IEEE), IKRAM UD DIN 3, (Senior Member, IEEE), MOHSEN GUIZANI 4, (Fellow, IEEE), AND MANSOUR ZUAIR5, “Spammer Detection and Fake User Identification on Social Networks”, IEEE Access, 2019.