Datasets:
Update README.md
Browse files# The Canceling Attitudes Detection Dataset (CADE)
The Canceling Attitudes Detection Dataset has been presented in the paper "That is Unacceptable: the Moral Foundations of Canceling"
## Abstract
Canceling is a morally-driven phenomenon that hinders the development of safe social media platforms and contributes to ideological polarization. To address this issue we present the Canceling Attitudes Detection (CADE) dataset, an annotated corpus of canceling incidents aimed at exploring the factors of disagreements in evaluating people canceling attitudes on social media. Specifically, we study the impact of annotators' morality in their perception of canceling, showing that morality is an independent axis for the explanation of disagreement on this phenomenon. Annotator's judgments heavily depend on the type of controversial events and involved celebrities. This shows the need to develop more event-centric datasets to better understand how harms are perpetrated in social media and to develop more aware technologies for their detection.
## Please cite this work if you use the dataset
"""
@misc
{lo2025unacceptablemoralfoundationscanceling,
title={That is Unacceptable: the Moral Foundations of Canceling},
author={Soda Marem Lo and Oscar Araque and Rajesh Sharma and Marco Antonio Stranisci},
year={2025},
eprint={2503.05720},
archivePrefix={arXiv},
primaryClass={cs.CY},
url={https://arxiv.org/abs/2503.05720},
}
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license: mit
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dataset_info:
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features:
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dtype: string
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splits:
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- name: train
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num_bytes: 4016857
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num_examples: 11935
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download_size: 1138689
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dataset_size: 4016857
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configs:
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- config_name: default
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data_files:
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- split: train
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path: data/train-*
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---
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license: mit
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dataset_info:
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features:
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- name: 'Unnamed: 0'
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dtype: int64
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- name: text_id
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dtype: string
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dtype: string
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- name: fairness
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dtype: string
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- name: annotation_id
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dtype: string
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splits:
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- name: train
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num_bytes: 4016857
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num_examples: 11935
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download_size: 1138689
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dataset_size: 4016857
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configs:
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- config_name: default
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data_files:
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- split: train
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path: data/train-*
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task_categories:
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- text-classification
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language:
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- en
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tags:
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- acceptability
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- stance
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- content-moderation
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- canceling
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- cancel-culture
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- hate-speech
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- moral-foundations-theory
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pretty_name: cade
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size_categories:
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- 10K<n<100K
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---
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