Real-Time Disaster Information Aggregation Software

Disaster response agencies often face challenges in obtaining real-time, actionable information during emergencies. Social media platforms provide vast streams of data that can be leveraged to enhance situational awareness. However, this data is plagued by issuessuch as misinformation and volume overload, making manual analysis inefficient. This paper proposes a frame- work utilizing Natural Language Processing (NLP), Machine Learning (ML), and automated alert systems to extract, classify, and disseminate disaster-relatedinformation from social media platforms. By integrating APIs, advanced analytics, and SMS- based notifications, the framework aims to expedite decision- making processes and improve emergency response efficiency.