@inproceedings{meunier-etal-2025-crisists,
title = "{C}risis{TS}: Coupling Social Media Textual Data and Meteorological Time Series for Urgency Classification",
author = "Meunier, Romain and
Benamara, Farah and
Moriceau, V{\'e}ronique and
Qiao, Zhongzheng and
Ramasamy, Savitha",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.783/",
doi = "10.18653/v1/2025.acl-long.783",
pages = "16082--16099",
ISBN = "979-8-89176-251-0",
abstract = "This paper proposes CrisisTS, the first multimodal and multilingual dataset for urgency classification composed of benchmark crisis datasets from French and English social media about various expected (e.g., flood, storm) and sudden (e.g., earthquakes, explosions) crises that have been mapped with open source geocoded meteorological time series data. This mapping is based on a simple and effective strategy that allows for temporal and location alignment even in the absence of location mention in the text. A set of multimodal experiments have been conducted relying on transformers and LLMs to improve overall performances while ensuring model generalizability. Our results show that modality fusion outperforms text-only models."
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%0 Conference Proceedings
%T CrisisTS: Coupling Social Media Textual Data and Meteorological Time Series for Urgency Classification
%A Meunier, Romain
%A Benamara, Farah
%A Moriceau, Véronique
%A Qiao, Zhongzheng
%A Ramasamy, Savitha
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F meunier-etal-2025-crisists
%X This paper proposes CrisisTS, the first multimodal and multilingual dataset for urgency classification composed of benchmark crisis datasets from French and English social media about various expected (e.g., flood, storm) and sudden (e.g., earthquakes, explosions) crises that have been mapped with open source geocoded meteorological time series data. This mapping is based on a simple and effective strategy that allows for temporal and location alignment even in the absence of location mention in the text. A set of multimodal experiments have been conducted relying on transformers and LLMs to improve overall performances while ensuring model generalizability. Our results show that modality fusion outperforms text-only models.
%R 10.18653/v1/2025.acl-long.783
%U https://aclanthology.org/2025.acl-long.783/
%U https://doi.org/10.18653/v1/2025.acl-long.783
%P 16082-16099
Markdown (Informal)
[CrisisTS: Coupling Social Media Textual Data and Meteorological Time Series for Urgency Classification](https://aclanthology.org/2025.acl-long.783/) (Meunier et al., ACL 2025)
ACL