Communication has become stronger due to exponential increase in the usage of social media in the last few years. People use them for communicating with friends, finding new friends, updating any important activities of their life, etc. Among different types of social media, most important are social networking sites and mobile networks. Due to their growing popularity and deep reach, these mediums are infiltrated with huge Vol.of spam messages. In this paper, we have discussed 5 traditional machine learning techniques for detecting spam in the short text messages on two datasets: SMS Spam Collection dataset taken from UCI Repository and Twitter dataset. Twitter dataset is compiled by crawling the public live tweets using Twitter API. The BoW with TF and TF-IDF weighing schemes is used for feature selection. The performance of various classifiers is evaluated with the help of metrics like precision, recall, accuracy and F1 score. The results show that the Random Forest gave highest accuracy with 100 estimators.

Spam Detection, machine learning, Traditional classifiers, Twitter spam, SMS spam, Text Classification

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