Deep Learning-Driven Detection of Terrorism Threats from Tweets Using DistilBERT and DNN

Abstract

As globalization accelerates, the threat of terrorist attacks poses serious challenges to national security and public safety. Traditional detection methods rely heavily on manual monitoring and rule-based surveillance, which lack scalability, adaptability, and efficiency in handling large volumes of real-time social media data. These approaches often struggle with identifying evolving threats, processing unstructured text, and distinguishing between genuine threats and misleading information, leading to delays in response and potential security lapses. To address these challenges, this study presents an advanced terrorism threat detection model that leverages DistilBERT with a Deep Neural Network (DNN) to classify Twitter data. The proposed approach efficiently extracts contextual and semantic information from textual content, enhancing the identification of potential terrorist threats. DistilBERT, a lightweight variant of BERT, is employed for its ability to process large volumes of text while maintaining high accuracy. The extracted embeddings are further analyzed using a Dense Neural Network, which excels at recognizing complex patterns. The model was trained and evaluated on a labeled dataset of tweets, achieving an impressive 93% accuracy. Experimental results demonstrate the model’s reliability in distinguishing between threatening and non-threatening tweets, making it an effective tool for early detection and real-time surveillance of terrorism-related content on social media. The findings highlight the potential of deep learning and natural language processing (NLP) in automated threat identification, surpassing traditional machine learning approaches. By integrating advanced NLP techniques, this model contributes to enhancing public safety, national security, and counter-terrorism efforts.