task_categories:text-classification size_categories:100K<n<1M language:es license:cc-by-4.0 region:us
---
license: cc-by-4.0
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: val
    path: data/val-*
  - split: test
    path: data/test-*
dataset_info:
  features:
  - name: Text
    dtype: string
  - name: Label
    dtype: int64
  - name: dataset
    dtype: string
  - name: __index_level_0__
    dtype: int64
  splits:
  - name: train
    num_bytes: 31442785
    num_examples: 136136
  - name: val
    num_bytes: 3542897
    num_examples: 15131
  - name: test
    num_bytes: 8671755
    num_examples: 37820
  download_size: 17952583
  dataset_size: 43657437
task_categories:
- text-classification
language:
- es
size_categories:
- 100K<n<1M
---

## Dataset Summary

The dataset consists of comments from several sources translated to Spanish language and classified as suicidal ideation/behavior and non-suicidal.

# Dataset Structure

The dataset has 175010 rows (77223 considered as Suicidal Ideation/Behavior and 97787 considered Not Suicidal).

## Dataset fields

* `Text`: User comment.
* `Label`: 1 if suicidal ideation/behavior; 0 if not suicidal comment.
* `Dataset`: Source of the comment

# Dataset Creation

* 112385 (84485 non suicidal, 27905 suicidal) from the [Suicide Watch dataset](https://www.kaggle.com/datasets/nikhileswarkomati/suicide-watch/).
* 46894 (46894 suicidal) from the [TwitterSuicidalAnalysis](https://github.com/IE-NITK/TwitterSuicidalAnalysis).
* 9919 (9183 non suicidal, 736 suicidal) from the corpus genereated in [Hackaton Somos NLP](https://huggingface.co/datasets/hackathon-somos-nlp-2023/suicide-comments-es)
* 8744 (4802 non suicidal, 3942 suicidal) from the paper [An Attention-based hybrid architecture with explainability for depressive social media text detection in Bangla](https://github.com/NM001007/An-Attention-based-Hybrid-Suicide-Ideation-Detection)
* 7084 (3559 non suicidal, 3525 suicidal) from the paper [Supervised Learning for Suicidal Ideation Detection in Online User Content](https://github.com/TabbieD/NLP-Sentiment-Analysis)
* 1972 (1540 non suicidal, 432 suicidal) from the paper [Detection of Suicidal Intent in Spanish Language Social Networks using Machine Learning](https://github.com/kvvaldez/spanish_suicide/blob/master/dataset/suicidio_notacion.csv)
* 1769 (1122 non suicidal, 647 suicidal) from the corpus [Suicidal Tweet Detection](https://www.kaggle.com/datasets/aunanya875/suicidal-tweet-detection-dataset/data)
* 316 (204 non suicidal, 112 suicidal) from the paper [Data Mining Approach to the Detection of Suicide in Social Media: A Case Study of Singapore](https://github.com/shingkid/data-mining-suicide-sg/tree/master)


# Considerations for Using the Data

## Social Impact of Dataset

The dataset could contain some patterns to detect suicidal ideation/behavior.

## Discussion of Biases

No measures have been taken to estimate the bias and toxicity embedded in the dataset. However, the most of the data is collected on Reddit, Twitter, and ChatGPT. So there is probably an age bias because [the Internet is used more by younger people](https://www.statista.com/statistics/272365/age-distribution-of-internet-users-worldwide).

# Additional Information

## Team

* [joheras](https://huggingface.co/joheras)






