Terrorist Activities Detection via Social Media Using Machine Learning

Perhaps the richest source of human generated text input is social media. Internet users' opinions, feedback, and criticisms represent their attitudes and feelings towards specific topics and concerns. The sheer volume of such information makes reading it difficult for any group of people. As a result, social media has become a major instrument for propagating their views and influencing or enticing people to join their terrorist actions in general. Social media is the most frequent and straightforward approach to contact a large number of individuals in a short period of time. This research focuses on the construction of a system that can detect terrorism-supporting tweets automatically through real-time analytics using the Apache Spark machine learning framework. The proposed approach is completely reliant on training data and attempts to enhance accuracy