  The identification of spam messages on social networks is a very challenging
task. Social media sites like Twitter \& Facebook attracts a lot of users and
companies to advertise and attract users of personal gains. These
advertisements most of the time leads to spamming, which in return leads to
poor user experience. The purpose of this paper is to undertake the analysis of
spamming on Twitter. To classify spams efficiently, it is necessary to first
understand the features of the spam tweets as well as identify attributes of
the spammer. We extract both tweet based features and user-based features for
our analysis and observe the correlation between these features. This step is
necessary as we can reduce the training time if we combine the highly
correlated features. Our proposed approach uses a classification model based on
artificial neural networks to classify the tweets as spam or non-spam giving
the highest accuracy of 97.57\% when compared with four other standard
classifiers namely, SVM, K Nearest Neighbours, Naive Bayes, and Random Forest.
