Work

Real-Time Disaster Information Aggregation Software

Year: 2025

Type: article

Abstract: 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.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.15313927

Author: Vansika Rathor, Siddhi Katkar, Simran Sharma

Language: English

Topic: Public Relations and Crisis Communication

Subfield: Communication

Field: Social Sciences

Domain: Social Sciences

Open Access status: green

OpenAlex ID: W6949562197

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