Using Natural Language Processing and Machine Learning to Detect Online Radicalisation in the Maldivian Language, Dhivehi

Early detection of online radical content is important for intelligence services to combat radicalisation and terrorism. The motivation for this research was the lack of language tools in the detection of radicalisation in the Maldivian language, Dhivehi. This research applied Machine Learning and Natural Language Processing (NLP) to detect online radicalisation content in Dhivehi, with the incorporation of domain-specific knowledge. The research used Machine Learning to evaluate the most effective technique for detection of radicalisation text in Dhivehi and used interviews with Subject Matter Experts and self-deradicalised individuals to validate the results, add contextual information and improve recognition accuracy. The contributions of this research to the existing body of knowledge include datasets in the form of labelled radical/non-radical text, sentiment corpus of radical words and primary interview data of self-deradicalised individuals and a technique for detection of radicalisation text in Dhivehi for the first time using Machine Learning. We found that the Naïve Bayes algorithm worked best for the detection of radicalisation text in Dhivehi with an Accuracy of 87.67%, Precision of 85.35%, Recall of 92.52% and an F2 score of 91%. Inclusion of the radical words identified through the interviews with SMEs as a count feature improved the performance of ML algorithms and Naïve Bayes by 9.57%.