DESIGN AND IMPLEMENTATION OF A MACHINE LEARNING BASED SYSTEM FOR THE DETECTION OF TWITTER BOTS

ABSTRACT

With the rapid growth of social media platforms like Twitter, the proliferation of bots has emerged as a significant concern. These automated accounts have the potential to disseminate misinformation and manipulate public opinion, underscoring the need for robust bot detection systems. This project, titled "Design and Implementation of a Machine Learning-Based System for the Detection of Twitter Bots," aims to conduct a comprehensive comparative analysis of machine learning algorithms specifically tailored for bot detection on Twitter. This research is motivated by the escalating concern surrounding bots on Twitter, which can undermine user trust and the authenticity of online conversations. While previous studies have explored bot detection, there is a lack of comprehensive comparative analyses of machine learning algorithms designed explicitly for bot detection on Twitter. Therefore, the primary goal of this project is to scrutinize the strengths and weaknesses of selected machine learning approaches, ultimately building an effective bot detection model that significantly addresses and attempts to solve some of the limitations of other Twitter bot detection systems.