Abstract: As the use of social media continues to grow, users increasingly share real-time information about their surroundings during emergencies and disasters. These user-generated posts, although unstructured, represent a valuable source of timely data that can significantly aid in disaster detection and emergency response. This study explores the application of natural language processing (NLP) techniques to extract relevant insights from social media posts and detect potential disasters as they unfold. By leveraging machine learning and linguistic analysis, the proposed approach can identify keywords, sentiments, and patterns indicative of emergencies. Furthermore, the paper introduces a comprehensive framework for real-time disaster detection using social media content. This framework integrates NLP models for text preprocessing, classification, and alert generation, ultimately enhancing the situational awareness and decision-making capabilities of emergency services, government agencies, transport companies and other stakeholders involved in crisis management.

Keywords: Natural Language Processing (NLP), Disaster Detection, Social Media Analysis, Emergency Response, Real-Time Monitoring, Text Classification, Crisis Management, Situational Awareness, Machine Learning, Unstructured Data Analysis

Pages: 730-739