Abstract: Millions of users throughout the world are active on social networking sites. Users' interactions with social media platforms like Twitter and Facebook have a significant impact on daily life, sometimes in unfavourable ways. Popular social networking sites have become a target for spammers who want to spread a tonne of harmful and unnecessary content. Twitter, for instance, has grown to be one of the most extravagantly used platforms ever and as a result, permits an excessive quantity of spam. False users spam users with unwanted tweets to advertise products or websites that not only negatively impact real users but also disturb resource usage. A popular field of research in today's online social networks is the identification of false Twitter users and the detection of spammers (OSNs). Review the procedures for identifying spammers on Twitter. In addition, a taxonomy of Twitter spam detection methodologies is offered, which groups the methods according to how well they can identify I phoney material, (ii) spam based on URL, (iii) spam in trending topics, and (iv) 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.
Keywords: Spam Detection, Fake user, online social networks, Detecting URL
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DOI:
10.17148/IJARCCE.2022.11631
