Detecting Multipliers of Jihadism on Twitter

Detecting terrorist related content on social media is a problem for law enforcement agency due to the large amount of information that is available. This work is aiming at detecting tweeps that are involved in media mujahideen - the supporters of jihadist groups who disseminate propaganda content online. To do this we use a machine learning approach where we make use of two sets of features: data dependent features and data independent features. The data dependent features are features that are heavily influenced by the specific dataset while the data independent features are independent of the dataset and can be used on other datasets with similar result. By using this approach we hope that our method can be used as a baseline to classify violent extremist content from different kind of sources since data dependent features from various domains can be added. In our experiments we have used the AdaBoost classifier. The results shows that our approach works very well for classifying English tweeps and English tweets but the approach does not perform as well on Arabic data.