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@article{164487,
        author = {K. Rithika Reddy and K. Sirija       and R. Rahul and G. Rakesh Reddy              },
        title = {Identifying Fake And Clone Accounts In Twitter},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {12},
        pages = {837-843},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=164487},
        abstract = {An online social network, or OSN, is a hub for social interaction among users who share similar interests or real-world connections. With OSN's growing popularity, security and privacy concerns are also growing in this regard. Users of social networks are facing serious security issues due to cloned and fake profiles. One major risk is user profile cloning, in which pre-existing user information is taken to make duplicate profiles, which are subsequently used maliciously to damage the identity of the original profile owner. They have the ability to initiate threats such as spamming, phishing, and stalking. A fake profile is a social media account created in the name of a person or company that does not actually exist with the intention of carrying out malicious activity. In this paper, a detection methodology has been proposed that can detect fake and duplicate profiles on Twitter. The detection of fake profiles is made using a set of rules for genuine and false profiles. Profile cloning can be detected by two methods: similarity measures and C4.5 decision tree algorithms. Similarity measures involve two types: network relationships and attribute similarities. C4.5 identifies clones by constructing a decision tree with information gained into consideration. To see how well these two approaches enhance the identification of clone profiles, a comparison is done.},
        keywords = {},
        month = {},
        }