---
title: "Introducing CAD: the Contextual Abuse Dataset — paper by Vidgen et al. (2021)"
description: "Automatic detection of online abuse is held back by training data that is small, coarse-grained, or labelled… 70 references, cited by 67 papers here."
authors: ["Bertie Vidgen", "Dong Nguyen", "Helen Margetts", "Patricia Rossini", "Rebekah Tromble"]
year: 2021
venue: "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies"
source: "https://aclanthology.org/2021.naacl-main.182/"
doi: "10.18653/v1/2021.naacl-main.182"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/vidgen-2021-introducing-cad-contextual-abuse-dataset/"
---
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# Introducing CAD: the Contextual Abuse Dataset

**Authors:** [Bertie Vidgen](https://md.research.elamur.ai/free/en/authors/bertie-vidgen/), [Dong Nguyen](https://md.research.elamur.ai/free/en/authors/dong-nguyen/), [Helen Margetts](https://md.research.elamur.ai/free/en/authors/helen-margetts/), Patricia Rossini and [Rebekah Tromble](https://md.research.elamur.ai/free/en/authors/rebekah-tromble/) · **Year:** 2021 · **Venue:** [Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies](https://md.research.elamur.ai/free/en/venues/naacl/) ([NAACL 2021](https://md.research.elamur.ai/free/en/venues/naacl/2021/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2021.naacl-main.182/) · [PDF](https://aclanthology.org/2021.naacl-main.182.pdf) · [DOI 10.18653/v1/2021.naacl-main.182](https://doi.org/10.18653/v1/2021.naacl-main.182) · [OpenAlex](https://openalex.org/W3168261013)

**Citations:** 80 citations (OpenAlex, 2026-10-14) · [67](https://md.research.elamur.ai/free/en/papers/vidgen-2021-introducing-cad-contextual-abuse-dataset/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
Automatic detection of online abuse is held back by training data that is small, coarse-grained, or labelled without regard to the surrounding discussion. Vidgen and colleagues address this with CAD, a collection of roughly 25,000 mostly English Reddit entries gathered via the Pushshift API. The annotation scheme is hierarchical. It has six primary classes: three abusive (Identity-directed, Affiliation-directed, Person-directed) and three non-abusive (Counter Speech, Non-hateful Slurs, Neutral). Finer secondary labels, such as Derogation or Dehumanization, sit beneath them. Annotators judged each entry while reading its whole conversation thread and recorded whether that context was needed. They also highlighted the relevant text spans as rationales. Disagreements were settled through expert-facilitated group discussion among trained annotators, not by majority voting. The authors provide baseline classifiers (logistic regression, BERT, DistilBERT) as reference points and note that the task remains difficult, partly because most entries are Neutral. Releasing the data, guidelines, models and code gives the field a richer, context-aware benchmark for building more nuanced abuse detectors.

## Key points
- CAD contains about 25,000 Reddit entries. One entry can carry several primary and/or secondary labels, and Neutral is meant to be exclusive of all others.
- Identity-directed and Affiliation-directed abuse share five subtypes: Derogation, Animosity, Threatening, Glorification and Dehumanization. Person-directed abuse is split by whether the target is in the thread or outside it.
- Each annotation records whether thread context was required. The authors describe this as the first abuse dataset to use context this deeply. Span-level rationales are also included.
- Baseline models sometimes break the label constraints by predicting Neutral together with another class: 3.8% of entries for DistilBERT, 5.4% for BERT and 10.7% for logistic regression.
- The project received ethics approval from The Alan Turing Institute, used paid in-house annotators with no crowdsourcing, and shows only synthetic examples. All resources are on GitHub at dongpng/cad_naacl2021.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Bertie Vidgen, Dong Nguyen, Helen Margetts, Patricia Rossini and Rebekah Tromble. 2021. Introducing CAD: the Contextual Abuse Dataset. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{vidgen2021introducing,
  title = {{Introducing CAD: the Contextual Abuse Dataset}},
  author = {Bertie Vidgen, Dong Nguyen, Helen Margetts, Patricia Rossini and Rebekah Tromble},
  booktitle = {Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies},
  year = {2021},
  doi = {10.18653/v1/2021.naacl-main.182},
  url = {https://doi.org/10.18653/v1/2021.naacl-main.182}
}
```

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*Updated 24 October 2026*