The Impact of Social Media in Terrorist Attack Detection: A Comprehensive Study

Terrorism is a complex and multifaceted threat that significantly affects the global security, frequently targeting innocent civilians to spread fear and advance ideological agendas. The proposed study focused on the use of machine learning (ML) and deep learning (DL) methods to identify terrorist activities on social media platforms. Terrorist organizations increasingly exploited social media for recruitment, propaganda, and attack coordination, necessitating the development of advanced tools to identify potential threats. While these models demonstrated improved accuracy and efficiency, the study identified significant challenges, such as handling data imbalance, distinguishing between legitimate and harmful content, and addressing the complexity of terrorist behavior across languages and regions. Ethical concerns, particularly false positives and the misclassification of naive conduct, were also highlighted as critical issues. The study concluded that while existing technologies offered valuable solutions, future models would need to incorporate real-time data, multilingual capabilities, and cross-regional analysis to enhance their effectiveness. Continuous updates and ethical considerations are essential for improving the detection and prevention of terrorist activities on social media platforms, contributing to global security efforts.