Twitter Bot Detection Using Machine Learning and Deep Learning Techniques

Abstract—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. Index Terms—Twitter Bot Detection, Machine Learning, Deep Learning, Social Network Analysis, Random Forest, Support Vec- tor Machine (SVM), Logistic Regression, K-Nearest Neighbors (KNN), Long Short-Term Memory (LSTM), Recurrent Neural Networks (RNN).