A Twitter Tale of Three Hurricanes: Harvey, Irma, and Maria

TL;DR

This paper analyzes millions of tweets from three major hurricanes using AI techniques to extract useful information, aiding disaster response and future automated systems.

Contribution

It presents a comprehensive multidimensional analysis of Twitter data during hurricanes using NLP and computer vision, highlighting information distributions for crisis management.

Findings

Revealed distributions of useful disaster-related information

Demonstrated effectiveness of AI techniques in social media analysis

Facilitated development of automated disaster response systems

Abstract

People increasingly use microblogging platforms such as Twitter during natural disasters and emergencies. Research studies have revealed the usefulness of the data available on Twitter for several disaster response tasks. However, making sense of social media data is a challenging task due to several reasons such as limitations of available tools to analyze high-volume and high-velocity data streams. This work presents an extensive multidimensional analysis of textual and multimedia content from millions of tweets shared on Twitter during the three disaster events. Specifically, we employ various Artificial Intelligence techniques from Natural Language Processing and Computer Vision fields, which exploit different machine learning algorithms to process the data generated during the disaster events. Our study reveals the distributions of various types of useful information that can inform crisis managers and responders as well as facilitate the development of future automated systems for disaster management.