Safeguarding Online Communications using DistilRoBERTa for Detection of Terrorism and Offensive Chats

الملخص

People use social media for both good and distasteful purposes. When used with malicious intent, it raises
significant concerns as it involves the use of offensive language and hate speech that promote terrorism and
other negative behaviors. To create a safe, secure and pleasant environment, these communications must be
closely monitored to prevent severe problems, associated risks and other pertinent issues. With the help of
AI, specifically Large Language Models (LLM), we can quickly analyze text and speech to determine whether
the communications promote the dangers identified here above not to mention other toxic elements. For this
research, the LLM used is the DistilRoBERTa model from the Transformers library using Hugging Face. The
DistilRoBERTa model was trained on datasets consisting of terrorism-related conversations, offensive-related
conversations, and neutral conversations. These datasets were obtained from publicly available sources. The
results of the experimentation show that the model achieved 99% accuracy, precision, recall, F1 score, and
ROC curve. To improve the robustness of the model, it must be continuously fine-tuned to predict dynamic
communication behavior since real conversations are inaccessible due to restrictions. A drag-and-drop
interface is used to upload the files and get the categorical output, ensuring seamless and easy interaction.