Twitter Bot Detection Using Machine Learning and Deep Learning Techniques

The proliferation of Twitter bots poses a serious threat to the reliability of online conversations and results in disinformation, spam, and opinion manipulation. This paper presents a comprehensive examination of Twitter bot detection techniques with traditional machine learning (ML) algorithms contrasted with cutting-edge deep learning (DL) models. Key fea- tures like tweet frequency, follower-following ratios, user behavior patterns, and content features are investigated. We compare algorithms like Random Forest, Support Vector Machines (SVM), Logistic Regression, K-Nearest Neighbors (KNN), Long Short- Term Memory (LSTM), and Recurrent Neural Networks (RNN) based on accuracy, precision, recall, and F1-score metrics. Our experiments showed that Random Forest was the best with the highest accuracy and thus, it is the best-suited model for the dataset used in this experiment. We also address the issues of real- time bot detection, the limitation of single models, and suggest a hybrid approach that takes advantage of the strengths of both ML and DL approaches for better performance.