Correlated Feature Selection for Tweet Spam Classification

TL;DR

This paper presents a correlated feature selection method combined with neural network classification to effectively identify spam tweets on Twitter, achieving high accuracy and reducing training time.

Contribution

It introduces a correlation-based feature selection approach for spam detection and demonstrates the effectiveness of neural networks over traditional classifiers.

Findings

Neural networks achieved 97.57% accuracy in spam classification.

Correlated feature reduction improved training efficiency.

Neural network outperformed SVM, KNN, Naive Bayes, and Random Forest.

Abstract

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.