---
task_categories:
- text-classification
language:
- en
- fr
tags:
- climate
pretty_name: 'CrisisTS'
size_categories:
- 10K<n<100K
---

# CrisisTS Dataset

## CrisisTS Description

CrisisTS is a multimodal multilingual dataset containing textual data from social media and meteorological data for crisis managmement.

### Dataset Summary

- **Languages**: 2 Languages (English and French)
- **Total number of tweets**: 22,291 (15,368 in French and 6,923 in English) (French textual data will be released soon)
- **Total number of French meteorological data**: 46,495 (3 hours frequency)
- **Total number of English meteorological data**: 1,460  (daily frequency)
- **Type of crisis** : Stroms, Hurricane, Flood, Wildfire, Explosion, Terrorist Attack, Collapse
- **Domain**: Crisis managment

### Dataset utilisation

To use the dataset please use

```unix
git clone https://huggingface.co/datasets/Unknees/CrisisTS
```

### Detailled English textual data information

<img src="English_Tabluar.png" alt="Centered Image" style="display: block; margin: 0 auto;" width="1000">

### Detailled French textual data information

<img src="French_Tabular.png" alt="Centered Image" style="display: block; margin: 0 auto;" width="1000">

### Data alignement 

All the textual data have been spatially aligned with the meteorological data with the following strategy :

1. If there is exactly one location mention in the text :

We use the keywords that we have in utils/Keywords in order to find in which state the location mention belongs.

2. If there is no location mention :

We use crisis_knowledge_LANG.csv to find the location of the tweet by association with the location of the impact of the crisis the tweets refer to.



### Raw Data and Adaptation
If you want to use only one modality, you can use the data contained in Textual_Data and Time_Series

The data inside Multi_modal_dataset are already merged with a fixed window for timeseries (48 hours window for French data and 5 day window for English data)

If you want to change the time series window you can use Linker_Fr.py and Linker_Eng.py. (WARNING : Linker_Fr can take some time)

To use the linker please use

```unix
python3 Linker_Eng.py --window_size 5 -output_file ./output_file.csv
```

or 

```unix
python3 Linker_FR.py -w 16 -o ./output_file.csv
```
With : 
-w / --window_size : the size of your timeseries window (with 3hours frequency for French data and daily data for English data)
-o / --output_file : path and name of your personnal dataset

Note that to launch the French linker, you will require the following librairies :
pandas
datetime
numpy
datetime
pytz
warnings
argparse


Note that to launch the English linker, you will require the following librairies :
pandas
os
json
scikit-learn
argparse

for more information on the dataset, please read readme.txt
### Citation Information

If you use this dataset, please cite:
```
@inproceedings{
title={Crisis{TS}: Coupling Social Media Textual Data and Meteorological Time Series for Urgency Classification},
author= "Meunier, Romain  and
      Benamara, Farah  and
      Moriceau, Veronique  and
      Zhongzheng, Qiao and
      Ramasamy, Savitha",
booktitle={The 63rd Annual Meeting of the Association for Computational Linguistics},
year={2025},

}
```