NARMADA: Need and Available Resource Managing Assistant for Disasters and Adversities
This addresses a critical gap in post-disaster relief coordination for affected communities, though it is incremental as it builds on existing social media and NLP methods.
The paper tackles the problem of identifying and matching resource needs and availabilities in post-disaster scenarios by presenting NARMADA, a semi-automated platform that uses NLP and IR techniques on social media data, resulting in a system that facilitates resource management for relief operations.
Although a lot of research has been done on utilising Online Social Media during disasters, there exists no system for a specific task that is critical in a post-disaster scenario -- identifying resource-needs and resource-availabilities in the disaster-affected region, coupled with their subsequent matching. To this end, we present NARMADA, a semi-automated platform which leverages the crowd-sourced information from social media posts for assisting post-disaster relief coordination efforts. The system employs Natural Language Processing and Information Retrieval techniques for identifying resource-needs and resource-availabilities from microblogs, extracting resources from the posts, and also matching the needs to suitable availabilities. The system is thus capable of facilitating the judicious management of resources during post-disaster relief operations.