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The digital sector is filled with numerous platforms that enable online communication.Apart from interruptions due to spam messages and security breaches, these have posed a significant challenge.The purpose of this paper is to provide a comprehensive way of dealing with the issue of spam on chat platforms.We did so by using machine learning algorithms such as Naive Bayes, Support Vector Machine (SVM), Logistic Regression, et cetera in order to see how effectively they can be used in identifying and filtering out spam messages.The study involved an extensive comparison between accuracy and precision metrics for evaluating the performance of these algorithms.These algorithm's strengths and limitations are shown through experimentation and analysis that give clues into what to consider when developing algorithms for detecting spam messages in various online chat platforms.In this light, we have successfully applied Naïve Bayes and logistic regression to text based Spam classification.After conducting thorough tests on it, the system had been able to identify 97% of all spams accurately resulting into a Precision Score of 1 thus enhancing trustworthiness and safety measures of online communication portals.Precision is preferred over accuracy due to imbalance in data.
Singh et al. (Sat,) studied this question.