cardiffnlp/twitter-roberta-large-sensitive-multilabel
Text Classification
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X-Sensitive is a multi-label dataset designed to identify sensitive language in social media. It consists of 7 labels and includes a total of 8,000 posts extracted from X. Each post is assigned one or more of the following labels based on its content: Drugs, Sex, Conflictual, Spam, Profanity, and Self-harm. More details in the reference paper.
The goal of X-Sensitive is to serve as a valuable resource for developing online moderation tools. The following models have been trained on X-Sensitive with this aim:
We also provide binary versions of the models, where each post is classified as either sensitive or not-sensitive:
Dataset Structure
Data Splits
| Name | #Entries |
|---|---|
| train | 5,000 |
| test | 2,000 |
| validation | 1,000 |
Data Instances
An example of train looks as follows.
{'#labels': 1,
'conflictual': 0,
'conflictual_highlight': array([], dtype=object),
'drugs': 0,
'drugs_highlight': array([], dtype=object),
'keyword': 'fuckin',
'labels': array(['profanity'], dtype=object),
'profanity': 1,
'profanity_highlight': array([array(['fucking'], dtype=object), array(['fucking'], dtype=object),
array(['fucking'], dtype=object)], dtype=object),
'selfharm': 0,
'selfharm_highlight': array([], dtype=object),
'sex': 0,
'sex_highlight': array([], dtype=object),
'spam': 0,
'spam_highlight': array([], dtype=object),
'text': 'i think the idea of aliens is so fucking cool'}
Labels
| Label Number | Label Name | Description |
|---|---|---|
| 0 | conflictual | Conflictual language. An attack based on protected (race, color, caster, gender, etc) or other categories. |
| 1 | profanity | Language containing slurs and profanity even if they are not directed towards a specific entity. |
| 2 | sex | Sexually Explicit Content. Pornographic or other types of sexual content. |
| 4 | selfharm | Self-harm. Posts depicting, promoting, or glorifying violence or harm against oneself such as eating disorders or suicide. |
| 5 | spam | Irrelevant content that is unsolicited. |
Citation Information
@article{antypas2024sensitive,
title={Sensitive Content Classification in Social Media: A Holistic Resource and Evaluation},
author={Antypas, Dimosthenis and Sen, Indira and Perez-Almendros, Carla and Camacho-Collados, Jose and Barbieri, Francesco},
journal={arXiv preprint arXiv:2411.19832},
year={2024}
}
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Models trained or fine-tuned on cardiffnlp/x_sensitive