Computer Science, Machine Learning and Big Data

Social networking websites have enjoyed a great success in recent years, apart from the numerous new opportunities that they are providing, extremist groups and terrorist organizations are using them to promote their ideology to facilitate internal communications and to evoke a planned psychological reaction in their enemies. Many web resources contain information about extremism, but a relatively small proportion comes from terrorist groups themselves and since manually monitoring and analyzing all their content separately during warfare is unattainable, solutions using automated methods are sought. This study applies machine learning techniques to perform automated extremist language detection. In this project, we proposed an approach for detecting extremist content and identifying potential extremist users in social media. The study’s methodology explores features in users’ histories to predict extremism via statistical topic model on an Arabic corpus which detects extremist posts with automatically generated features and a graded structure in which, whether extremism applies to a given person is a matter of degree related to multiple factors. To demonstrate our work, we created a dataset containing over 360,000 web forum posts. Experiments on a sampled data set show precision of 96.20% and recall of 94.90%.