Detect terroristic behavior on social media using machine learning approaches
Due to the increase of the number of users on the web, it becomes easier to exchange and analyze their data. This system provides a way about how to detect the tendency of terrorism on social web mainly Twitter. Jihadist groups like ISIS are spreading online propaganda using various forms of social media such as Twitter and YouTube. One of the most common methods of stopping these groups is to suspend (temporarily block) accounts that spread propaganda when they are discovered. However, this method requires human analysts to manually scan and analyze a massive amount of information on social media. This system provides a way to do this automatically (detect radical content that is released by jihadist groups) on Twitter. The main idea of this system is to predict whether a person has the tendency to be a terrorist or not. Machine learning (ML) and Natural Language processing (NLP) approaches are used to get a preliminary determination of the terrorism tendency by analyzing public tweets published on Twitter. In this system, three machine learning algorithms are used ( Support Vector Machine (SVM), Naive Bays and AdaBoost ) ,the Accuracy of the three algorithms are compared to decide which one is better when using our data-sets. As a conclusion our system aims to reduce the increase of of distributing terrorism on the social media (Twitter). Finally will discuss how our system can be modified to work on other behaviors , and to contribute in other manners in the human behavior researches.