Using Twitter Data to Determine Hurricane Category: An Experiment

TL;DR

This paper explores how Twitter data can be used to assess and predict hurricane severity levels by analyzing posts during Hurricanes Harvey and Irma, demonstrating a positive correlation and proposing a predictive method.

Contribution

It introduces a novel approach to correlate social media data with hurricane severity and presents a method to predict hurricane categories using Twitter data.

Findings

Positive correlation between Twitter data and hurricane severity

Method for predicting hurricane category from social media data

Potential for real-time disaster assessment using social media

Abstract

Social media posts contain an abundant amount of information about public opinion on major events, especially natural disasters such as hurricanes. Posts related to an event, are usually published by the users who live near the place of the event at the time of the event. Special correlation between the social media data and the events can be obtained using data mining approaches. This paper presents research work to find the mappings between social media data and the severity level of a disaster. Specifically, we have investigated the Twitter data posted during hurricanes Harvey and Irma, and attempted to find the correlation between the Twitter data of a specific area and the hurricane level in that area. Our experimental results indicate a positive correlation between them. We also present a method to predict the hurricane category for a specific area using relevant Twitter data.