Social Response and Disaster Management: Insights from Twitter Data Assimilation on Hurricane Ian

Karimiziarani, Mohammadsepehr; Moradkhani, Hamid

doi:10.2139/ssrn.4292734

Cited by 3 publications

(1 citation statement)

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“…Using diverse classifiers, including machine learning and statistical analysis, articles during the eruption were classified, uncovering distinctions between local and foreign news. A study (Karimiziarani and Moradkhani 2023) investigates the public response to hurricane Ian (Sep 2022) using NLP techniques on over twenty million Twitter tweets in the contiguous US. By employing sentiment analysis, topic modelling, and text classification, the study unveils key discussion topics, their interrelations, and categorizes tweets into humanitarian themes.…”

Section: Literature Reviewmentioning

confidence: 99%

Machine Learning Models for Mining Social Media Data for Effective Natural Disaster Assessment

Mittal,

Karki,

Parasher

et al. 2023

Preprint

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Satellite technology has emerged as a key tool for effective management and assessment of natural disasters. However, the challenge of accurately estimating impacted populations and assessing building damage, often obscured from aerial views, persists. To address this, the integration of imagery and textual data from social networks offers a promising solution. This study employs Twitter and Flickr datasets, using SVM, CNN, XGBoost, Logistic Regression, and Gradient Boost to extract insights. The sentiment analysis component categorizes disaster-affected individuals' emotions as panic, neutral, or non-panic. The Logistic Regression model excels in text classification, boasting an impressive 88.99% accuracy on the test dataset and 83.45% on training. The framework introduces an Aid analysis model, which gives us an impressive accuracy of 83.16% for the classification of tweets based on the aid sought by people through tweets. Image classification employs CNN, achieving 83.29% accuracy to comprehend disaster impact visually. Given real-time social media responses, the system assists government and organisations promptly, prioritising assistance. It serves as a dependable aid resource, enabling efficient responses tailored to affected communities. Thus, this approach holds potential to significantly enhance disaster relief efficacy.

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Section: Literature Reviewmentioning

confidence: 99%