Extremism Detection System: A Hybrid Machine Learning and Deep Learning Framework for Online Extremism Detection

The rapid growth of online communication platforms has significantly increased the spread of extremist and harmful

content. Traditional moderation techniques based on keyword filtering and manual review are insufficient for largescale

monitoring and lack contextual understanding. This paper proposes a hybrid extremism detection system that integrates

machine learning, deep learning, and rule-based approaches to identify extremist text with improved accuracy and

interpretability.

The proposed framework combines TF-IDF–based statistical learning, a Bidirectional Long Short-Term Memory (BiLSTM)

network for sequential modeling, and a DistilBERT transformer for contextual understanding. A priority override mechanism

ensures that high-confidence contextual predictions are not diluted by averaging model outputs. Additionally, an explainable

dashboard interface provides real-time risk scores and highlights influential keywords to support human moderators.

Experimental evaluation demonstrates that the hybrid approach improves precision, recall, and F1-score compared to

standalone models. The system also supports real-time deployment through a web-based interface, making it suitable for

practical content moderation applications. The results indicate that integrating statistical, sequential, and contextual modeling

provides a scalable and interpretable solution for detecting extremist content in online environments