DISASTER DETECTION THROUGH SOCIAL MEDIA POST ANALYSIS

Abstract

In recent years, social media platforms have become vital sources of real-time information during natural and man-made disasters. This study focuses on developing an intelligent system for disaster detection and monitoring by analyzing social media posts using machine learning and natural language processing (NLP) techniques. The proposed approach collects and filters user-generated content such as tweets, posts, and images to identify early signs of earthquakes, floods, fires, and other emergencies. Text classification, sentiment analysis, and keyword extraction are used to distinguish disaster-related posts from irrelevant data. By leveraging deep learning models for accurate event recognition, the system provides rapid situational awareness and supports emergency response teams in decision-making. This research aims to enhance disaster management efficiency by offering a cost-effective, scalable, and real-time solution for crisis detection through social media analytics.