Multilingual Text Preprocessing and Classification For The Detection Of Extremism And Radicalization In Social Networks

Due to the lack of strict controls on social networks, extremist groups like ISIS, Al-Qaeda, and white supremacists have taken advantage of these platforms to spread their ideas, distribute harmful content, and recruit new members. The information distributed through these channels is comprehensive, diverse, and conveyed in multiple languages. The study of online extremism and radicalization is a multifaceted and intricate area of research. The efficacy of machine learning, deep learning, and natural language processing (NLP) algorithms has been demonstrated. Although the majority of research in this field focuses on the analysis of data in a single language, there needs to be more studies on the analysis of multilingual data, specifically about detecting extremism in multilingual material. This research paper introduces the fabrication of an artificial intelligence system that leverages multilingual text posts from social networks to identify instances of extremism and radicalization. We utilize natural language processing (NLP) linguistic methods and text classification to identify extremism and radicalization in text data. Our study results are outstanding. The Bi-LSTM (Bidirectional et al.) model demonstrates a binary classification accuracy of 97.33%, and the multiclass classification accuracy of the Transformer-based model, which employs the DistilBERT-multi (Distilled version of the Multilingual Bidirectional Encoder Representations from Transformers) pre-trained model, is 91.07%. The findings above show significant progress in multiclass multilingual text classification and the detection of extremism and radicalization within social networks.