DisKnow: A Social-Driven Disaster Support Knowledge Extraction System

João Boné,Mariana Dias,João C Ferreira,Ricardo Ribeiro

doi:10.3390/app10176083

Abstract

This research is aimed at creating and presenting DisKnow, a data extraction system with the capability of filtering and abstracting tweets, to improve community resilience and decision-making in disaster scenarios. Nowadays most people act as human sensors, exposing detailed information regarding occurring disasters, in social media. Through a pipeline of natural language processing (NLP) tools for text processing, convolutional neural networks (CNNs) for classifying and extracting disasters, and knowledge graphs (KG) for presenting connected insights, it is possible to generate real-time visual information about such disasters and affected stakeholders, to better the crisis management process, by disseminating such information to both relevant authorities and population alike. DisKnow has proved to be on par with the state-of-the-art Disaster Extraction systems, and it contributes with a way to easily manage and present such happenings.

Highlights

Throughout the world, earthquakes, floods, fires, and other natural hazards cause tens of thousands of deaths and billions of euros in economic losses each year [1]
Taking into account the work performed on disaster monitoring through social media, and these mentioned techniques, we identified a gap in the way knowledge is being delivered to stakeholders, as most systems use social media for extracting data that only later is reported as a whole
DisKnow builds upon existing tools, contributing with a dynamic way of extracting and representing spatial and temporal relationships, as well as providing this knowledge to decision-makers, in real-time

Summary

Introduction

Throughout the world, earthquakes, floods, fires, and other natural hazards cause tens of thousands of deaths and billions of euros in economic losses each year [1]. Advances in information technology and communications, combined with the introduction and upsurge of social media apps, have created a new world of emergency and disaster management services by allowing impacted people to produce real-time georeferenced information on critical incidents [2]. In social networks, such as Twitter, Facebook, Instagram, and others, humans volunteer plentiful and free information about their surrounding environments. One of the obstacles of being able to use this information is filtering it, considering social media posts tend to vary widely in both their subjects and utility, ranging from off-topic to relevant disaster-related information

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Conclusion