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Lookup NU author(s): Dr Diana Contreras Mojica, Professor Sean Wilkinson, Professor Philip James
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© The Author(s) 2021.Memorial days of disasters represent an opportunity to evaluate the progress of recovery. This article uses sentiment analysis (SA) to assess post-disaster recovery on the 10th anniversary of L’Aquila’s earthquake using Twitter data. We have analyzed 4349 tweets from 4 to 10 April 2019 with the hashtag: #L’Aquila that we have obtained from a third-party vendor. The polarity is first defined using a supervised classification based on experts’ rules on post-disaster reconstruction and Grammarly tones. Then, this polarity is compared with the outcome of an unsupervised classification based on the pre-trained SA machine learning algorithm developed by MonkeyLearn. We have found a significant negative assessment of the post-disaster recovery process in L’Aquila. About 33.1% of the tweets had a negative polarity, followed by 29.3% tweets with a neutral polarity, 28.7% with positive polarity, and 8.9% unrelated to the anniversary. Further analysis of the tweets confirms that after 10 years, the reconstruction is still ongoing and that criticism of the recovery reported in the literature is also found in the tweets. Based on our analysis, the critical day to collect most of the data is the anniversary’s exact day. Tweets from citizens and/or news agencies, which are more likely to express the reality experienced, are therefore more useful in understanding recovery than tweets from government officials and/or governmental institutions. From the total 4349 tweets, we can state that 2488 (57%) were correctly classified by the pre-trained SA machine learning algorithm developed by MonkeyLearn, while 1861 (43%) were misclassified. It means an overall accuracy (ACC) of 57% and a misclassification rate of 43% by the algorithm. We argue that our results have the potential to serve as a benchmark that can be used to compare other post-disaster recovery processes using the same Twitter-based SA on their anniversaries.
Author(s): Contreras D, Wilkinson S, Balan N, James P
Publication type: Article
Publication status: Published
Journal: Earthquake Spectra
Year: 2022
Volume: 38
Issue: 1
Pages: 81-108
Print publication date: 01/02/2022
Online publication date: 14/08/2021
Acceptance date: 07/07/2021
Date deposited: 25/08/2021
ISSN (print): 8755-2930
ISSN (electronic): 1944-8201
Publisher: SAGE Publications Inc.
URL: https://doi.org/10.1177/87552930211036486
DOI: 10.1177/87552930211036486
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