Loading [MathJax]/jax/output/HTML-CSS/fonts/TeX/fontdata.js
Preprints
https://doi.org/10.5194/egusphere-2024-2556
https://doi.org/10.5194/egusphere-2024-2556
30 Aug 2024
 | 30 Aug 2024

Content Analysis of Multi-Annual Time Series of Flood-Related Twitter (X) Data

Nadja Veigel, Heidi Kreibich, Jens A. de Bruijn, Jeroen C. J. H. Aerts, and Andrea Cominola

Abstract. Social media can provide insights into natural hazard events and people's emergency responses. In this study, we present a natural language processing analytic framework to extract and categorize information from of 43,287 Twitter (X) posts in German since 2014. We implement Bidirectional Encoder Representations from Transformers in combination with unsupervised clustering techniques (BERTopic) to automatically extract social media content, addressing transferability issues that arise from commonly used bag-of-word representations. We analyze the temporal evolution of topic patterns, reflecting behaviors and perceptions of citizens before, during, and after flood events. Topics related to low-impact riverine flooding contain descriptive hazard-related content, while the focus shifts to catastrophic impacts and responsibilities during high-impact events. Our analytical framework enables analyzing temporal dynamics of citizens’ behaviors and perceptions which can facilitate lessons learned analyses and improve risk communication and management.

Competing interests: One of the co-authors is part of the NHESS editorial board.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Share

Journal article(s) based on this preprint

26 Feb 2025
Content analysis of multi-annual time series of flood-related Twitter (X) data
Nadja Veigel, Heidi Kreibich, Jens A. de Bruijn, Jeroen C. J. H. Aerts, and Andrea Cominola
Nat. Hazards Earth Syst. Sci., 25, 879–891, https://doi.org/10.5194/nhess-25-879-2025,https://doi.org/10.5194/nhess-25-879-2025, 2025
Short summary
Download

The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.

Short summary
This study explores how social media, specifically Twitter (X), can help understand public...
Share