Abstract:

In this article, we explore various approaches to detecting spam on Twitter and provide a taxonomy of the different methods employed. We demonstrate that four key techniques are effective for classifying fraudulent user IDs: (i) identifying fake content, (ii) detecting spam at the URL level, (iii) analyzing spam in trending topics, and (iv) recognizing fake user profiles. These methods collectively aid in identifying and tracking spammers on the platform.

Description:

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