Abstract:
The Social Media Bot Detection System (SMBDS) is an innovative solution designed to address the growing prevalence of bot-driven activities on Twitter, a platform central to global communication and information sharing. This system provides an integrated approach to identifying, analyzing, and managing suspicious accounts, ensuring the authenticity and integrity of interactions. By replacing traditional manual monitoring methods, SMBDS facilitates real-time data sharing and automated detection of bot-like behaviors, enhancing efficiency, accuracy, and transparency. Built on [state the technology platform, e.g., Python, etc.], the system employs advanced data scraping, machine learning algorithms, and behavioral analysis tailored to Twitter’s unique ecosystem to detect anomalies such as spamming and repetitive posting. With minimal human intervention, administrators can easily flag, unflag, and update the statuses of suspicious accounts using an intuitive interface, ensuring rapid and informed responses to potential threats. Access to real-time analysis enables better decision-making, reducing manual errors and ensuring effective resource allocation. This project addresses critical challenges, including slow manual detection, lack of transparency, and the inefficiencies of traditional monitoring, by automating the identification and mitigation of malicious bot activities. The Social Media Bot Detection System offers Twitter a robust tool for improving platform safety, preserving user trust, and fostering an authentic and secure environment for communication.
