Twitter Analytics of Three Hurricanes in 2017
Harvey, Irma and Maria
Bibliographic Data
| ID | 7158916 |
|---|---|
| Authors | Sue Abdinnour (0009-0002-4799-8114, Department of Finance, Real Estate, and Decision Sciences, W. Frank Barton School of Business Wichita State University Wichita Kansas USA, corresponding author), Sesan Adeniji (0000-0002-9739-2893, Department of Finance, Real Estate, and Decision Sciences, W. Frank Barton School of Business Wichita State University Wichita Kansas USA), Sujay Daggubati (0009-0006-1800-5637, Department of Finance, Real Estate, and Decision Sciences, W. Frank Barton School of Business Wichita State University Wichita Kansas USA) |
| Year | 2025 |
| Volume | 33 |
| Issue | 4 |
| Publication date | 2025-12-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Journal of Contingencies and Crisis Management (JOURNAL) |
| Journal identifiers | ISSN: 0966-0879 • E-ISSN: 1468-5973 |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1111/1468-5973.70102 |
| OpenAlex | W7115918616 |
| Language | EN |
| References cited | 37 |
This study explores how Twitter functioned as a channel for public communication, emotional expression and situational awareness during the 2017 hurricanes Harvey, Irma and Maria. Using the Twitter API and AWS Elastic MapReduce, a dataset of 203,320 English‐language hurricane‐related Tweets was collected. The analysis combined a customised AFINN‐based lexicon for sentiment evaluation, comparative topic modelling through latent Dirichlet allocation (LDA) and K‐Means clustering, and spatial statistical methods to detect geographic clustering. Non‐parametric tests, including the Kruskal–Wallis and Mann–Whitney U tests, were used to compare sentiment differences across storms, while chi‐square, ANOVA and Kruskal–Wallis analyses examined topic distribution. The findings showed significant differences among hurricanes. Harvey and Irma displayed a clear rebound from negative to positive sentiment after landfall, reflecting hope and early recovery, while Maria remained predominantly negative, signalling extended hardship and dissatisfaction with response efforts. LDA performed better than K‐Means, producing coherent seven‐topic structures that revealed both shared themes, such as hurricane category, environmental impact, community support and weather updates and unique topics for each event, including presidential response for Harvey, evacuation readiness for Irma and power outages for Maria. Geographical analysis using heatmaps and Moran's I confirmed significant clustering for Harvey and Irma, but diffuse patterns for Maria. Overall, the study demonstrates that integrating sentiment, thematic and spatial analyses provides a multidimensional understanding of disaster communication and recommends that emergency managers employ such frameworks for real‐time situational monitoring, geographically targeted relief and adaptive communication strategies to enhance resilience and public trust
Analytics · Cluster analysis · Emergency management · Latent Dirichlet allocation · Sentiment analysis · Situation awareness · Situational ethics · Thematic analysis · Topic model · Disaster Management and Resilience · Public Relations and Crisis Communication · Tropical and Extratropical Cyclones Research
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Exploring the Space of Topic Coherence Measures
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| Highly cited | No |
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