---
license: mit
dataset_info:
  features:
  - name: text_id
    dtype: string
  - name: target
    dtype: string
  - name: text
    dtype: string
  - name: annotator_id
    dtype: string
  - name: stakeholder
    dtype: string
  - name: stance
    dtype: float64
  - name: acceptability
    dtype: float64
  - name: sample
    dtype: string
  - name: comment
    dtype: string
  - name: care
    dtype: float64
  - name: fairness
    dtype: float64
  - name: loyalty
    dtype: float64
  - name: authority
    dtype: float64
  - name: purity
    dtype: float64
  - name: cluster
    dtype: int64
  - name: Gender identity
    dtype: string
  - name: annotation_id
    dtype: string
  splits:
  - name: train
    num_bytes: 3921377
    num_examples: 11935
  download_size: 1073193
  dataset_size: 3921377
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
task_categories:
- text-classification
language:
- en
tags:
- acceptability
- stance
- content-moderation
- canceling
- cancel-culture
- hate-speech
- moral-foundations-theory
pretty_name: cade
size_categories:
- 10K<n<100K
---

# The Canceling Attitudes Detection (CADE) Dataset
CADE is a dataset created in the context of the research [**That is Unacceptable: the Moral Foundations of Canceling**](https://arxiv.org/pdf/2503.05720). Here you can find the 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.


## If you use the dataset please cite this work

```

@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}, 
}
```