A Comparative Study of Detecting Twitter Spam using Deep Learning

Abstract

This study addresses the escalating challenge of Twitter spam detection by leveraging the power of Convolutional Neural Networks (CNNs). With the proliferation of spam content on social media platforms, traditional machine learning algorithms have exhibited limitations in discerning intricate patterns within sequential data. The research problem centers on the need for a more robust and effective approach to distinguish spam tweets from legitimate content. The primary objective is to evaluate the performance of CNNs in comparison to baseline algorithms, including SVM, Decision Tree, KNN, Gaussian Naive Bayes, and Gradient Boosting. The research approach involves thorough data preprocessing, followed by model training and assessment using metrics like Confusion Matrix and Classification Report. The outcomes indicate that the CNN model outperforms the baseline algorithms, exhibiting superior levels of accuracy, precision, recall, and F1-score. These results highlight the promise of CNNs in reshaping the landscape of Twitter spam detection, presenting a more precise and effective approach to tackle the spread of spam content across social media platforms.This research contributes valuable insights for the development of advanced machine learning techniques in the domain of online security and spam detection.