Spammer Detection And Fake User Identification On Social Networks

Spammers who seek to disseminate tonnes of dangerous and useless material have turned popular social networking sites into a target. For instance, Twitter has become one of the most frequently utilised platforms ever and enables an excessive amount of spam as a result. The detection of spammers and the identifying of fake Twitter users is a major area of research in today’s online social networks. In this paper, we examine the techniques for identifying Twitter spammers. A supervised machine learning-based strategy for spammer identification is proposed in this study. Gathering a dataset from Twitter that contains user messages is the first and most important stage in the procedure. to separate spammers from other users. Then, utilise the SVM (Support Vector Machines) algorithm, random forest algorithm, and decision trees to apply a collection of features from the content of messages and users’ social interactions to the detection of spammers. With a true positive rate of 87.1% for spammers and 88.9% for non-spammers, the experiment shows the recommended solution’s capacity to provide excellent performance.