FAKE PROFILE IDENTIFICATION IN SOCIAL NETWORK USING MACHINE LEARNING AND NLP
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Abstract
Worldwide, social networking services are used by millions of people. The way users interact with social media platforms like Twitter and Facebook has a significant impact on daily life, often with negative outcomes. Popular social networking sites have been used as a target by spammers to spread a lot of harmful and irrelevant content. For instance, Twitter has become one of the most widely used platforms ever, which has led to an overwhelming amount of spam. Fake users waste resources and hurt real users by sending unwanted tweets to users in order to promote businesses or websites. Additionally, the capacity for disseminating false information to users using fictitious identities has increased, contributing to the proliferation of dangerous items. In today's online social networks, finding spammers and fraudulent users on Twitter has recently become a hot research area (OSNs). phoney content, based on URL spam, Trending topics with spam and fake users. The presented techniques are also contrasted based on a number of criteria, including user, content, graph, structure, and time factors. We are optimistic that the study that has been provided will serve as a beneficial tool for scholars looking for the most significant recent advancements in Twitter spam detection on a single platform.
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