Post-Disaster Recovery Assessment Using Sentiment Analysis of English-Language Tweets: A Tenth-Anniversary Case Study of the 2010 Haiti Earthquake

Diana Contreras (), Dimosthenis Antypas, Javier Hervas, Sean Wilkinson, Jose Camacho-Collados, Philippe Garnier and Cécile Cornou
Additional contact information
Diana Contreras: School of Earth and Environmental Sciences, Cardiff University, Cardiff CF10 3AT, UK
Dimosthenis Antypas: School of Computer Sciences, Cardiff University, Cardiff CF24 4A, UK
Javier Hervas: Independent Researcher, Cardiff CF10 2HS, UK
Sean Wilkinson: School of Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
Jose Camacho-Collados: School of Computer Sciences, Cardiff University, Cardiff CF24 4A, UK
Philippe Garnier: AE&CC Research Unit, CRAterre Research Lab, École Nationale Supérieure d’Architecture de Grenoble, Université Grenoble Alpes, 12636 Grenoble, France
Cécile Cornou: ISTerre, IRD, Université Grenoble Alpes, 38000 Grenoble, France

Sustainability, 2025, vol. 17, issue 11, 1-32

Abstract: The 2010 Haiti earthquake stands as one of the most catastrophic events in terms of loss of life and destruction. Following an earthquake, there is an urgent demand for information. Regrettably, few studies have tracked the progress of the post-disaster recovery, leaving this phase poorly understood. In previous years, data were exclusively collected through on-site missions, but today, social media (SM) has enhanced earthquake reconnaissance teams’ capacity to collect data beyond the emergency phase. However, text data from SM is unstructured, making it necessary to use natural language processing techniques to extract meaningful information. Sentiment analysis (SA), which classifies people’s opinions into positive, negative, or neutral polarity, is a promising tool for understanding earthquake recovery. For the purposes of this paper, we conduct SA at the tweet level on data collected around the tenth anniversary of the earthquake using human expertise to fine-tune automatic classification methods. We conclude that the anniversary date is the best time to collect data. In our sample, 56.3% of the tweets in the sample were classified as negative, followed by positive (27.3%), neutral (8.2%), and unrelated (8.1%). In our study, we conclude that the assessment of the recovery progress based on data collected from Twitter is negative. The automatic method for SA with the highest accuracy is ‘btweet’. The assessment result must be validated by stakeholders.

Keywords: Haiti; earthquakes; post-disaster recovery; social media (SM); Twitter; natural language processing (NLP); sentiment analysis (SA); reconstruction; vulnerability; funding mismanagement (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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