Using a Content-Based Filtering Approach, Advanced Study on Spam Detection and Spammer Behavior Analysis on Twitter
Because Twitter is one of the most widely used social media platforms, it is prone to abuse. Spamming is one of the ways that people abuse Twitter. Spam becomes a problem when a communication medium, particularly one that allows worldwide connection and handles large amounts of online data, becomes a problem. Because Twitter is so widely used, it makes it easier for spammers to thrive. Spammers are those who send unsolicited messages to others in order to either market a product or persuade them to click on dangerous links that may harm their computer systems. The primary goal of these spammers is to gain money off of their victims. Several systems have been developed in recent years with the goal of assessing whether or not a user is a spammer. However, these systems are unable to filter every spam message, and a spammer can create a new account and utilise it to send further messages. The content-based technique proposed in this paper can be used to filter spam tweets. To filter out unwanted tweets, the method involves combining tweets with machine learning and compression methods. The technology will continue to advance, assisting in the removal of spammers and the improvement of Twitter space. Spam detection has become an important aspect of Twitter’s security strategy for protecting users from cyber criminals and other unwanted actors.
Author (S) Details
Department of Computer Science, Sri Ramakrishna College of Arts and Science (Autonomous), Nava India, Coimbatore-641021, Tamil Nadu, India.
Department of Computer Science, Thiagarajar College, Madurai-625009, Tamil Nadu, India.
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