ISSN 2394-5125

    Extreme Learning Machine for Spammer Detection and Fake User Identification from Twitter (2023)

    P. Hemalatha, N. Ragapriya, V. Sanjana, K. Shiva Bhavani
    JCR. 2023: 184-192


    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). This project proposes the detection of spammers and fake user identification on Twitter data using deep learning mechanism called extreme learning machine (ELM) and compared the obtained results with various machine learning algorithms like random forest, naevi bayes and support vector machine. 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


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    Volume & Issue

    Volume 10 Issue-3