---
title: "jigsaw-unintended-bias-in-toxicity-classification"
description: "4 papers in the tracked journals link to the Kaggle dataset jigsaw-unintended-bias-in-toxicity-classification in their Crossref records, the first in 2023; most of them (2) in 2025."
url: https://scholar4ai.com/code/kaggle-competition-jigsaw-unintended-bias-in-toxicity-classification
markdown_url: https://scholar4ai.com/code/kaggle-competition-jigsaw-unintended-bias-in-toxicity-classification.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "4 papers in the tracked journals link to the Kaggle dataset jigsaw-unintended-bias-in-toxicity-classification in their Crossref records, the first in 2023; most of them (2) in 2025. (as of 2026-11-03)"
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-11-03T14:43:26+09:00
window: 2026-10-05 to 2026-11-03
source: OpenAlex (CC0)
---

# jigsaw-unintended-bias-in-toxicity-classification: papers linking to it

Research papers in the 480 tracked journals that link to jigsaw-unintended-bias-in-toxicity-classification (https://www.kaggle.com/competitions/jigsaw-unintended-bias-in-toxicity-classification), read from their Crossref records: by year, category and journal, the family tree of papers building on one another, and the linked repositories derived from it or that it derives from.

- Kaggle
- https://www.kaggle.com/competitions/jigsaw-unintended-bias-in-toxicity-classification

All repositories: https://scholar4ai.com/code.md

## Key findings

- 4 papers in the tracked journals link to the Kaggle dataset jigsaw-unintended-bias-in-toxicity-classification in their Crossref records, the first in 2023; most of them (2) in 2025.
- By category: AI (4); the journals with the most are Machine Learning (1), Cognitive Computation (1) and Language Resources and Evaluation (1).

## By year

*Papers linking to jigsaw-unintended-bias-in-toxicity-classification by publication year*

| Year | Papers |
|---|---:|
| 2023 | 2 |
| 2025 | 2 |

## Journals

*Journals whose papers link to jigsaw-unintended-bias-in-toxicity-classification most*

| Journal | Papers |
|---|---:|
| [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | 1 |
| [Cognitive Computation](https://scholar4ai.com/journals/cognitive-computation.md) | 1 |
| [Language Resources and Evaluation](https://scholar4ai.com/journals/language-resources-and-evaluation.md) | 1 |
| [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | 1 |

## Papers

*Papers linking to jigsaw-unintended-bias-in-toxicity-classification, most cited first*

| Published | Title | Journal | Authors | Citations | Link |
|---|---|---|---|---:|---|
| 2023-08-23 | [Persian offensive language detection](https://doi.org/10.1007/s10994-023-06370-5) | [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | Emad Kebriaei, Ali Homayouni, Roghayeh Faraji et al. | 16 | cites it |
| 2023-10-21 | [Beyond plain toxic: building datasets for detection of flammable topics and inappropriate statements](https://doi.org/10.1007/s10579-023-09682-z) | [Language Resources and Evaluation](https://scholar4ai.com/journals/language-resources-and-evaluation.md) | Nikolay Babakov, Varvara Logacheva, Alexander Panchenko | 8 | cites it |
| 2025-02-01 | [Discovering the Cognitive Bias of Toxic Language Through Metaphorical Concept Mappings](https://doi.org/10.1007/s12559-025-10423-3) | [Cognitive Computation](https://scholar4ai.com/journals/cognitive-computation.md) | Mengshi Ge, Rui Mao, Erik Cambria | 7 | cites it |
| 2025-05-26 | [Underrepresentation, label bias, and proxies: Towards Data Bias Profiles for the EU AI act and beyond](https://doi.org/10.1016/j.eswa.2025.128266) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | Marina Ceccon, Giandomenico Cornacchia, Davide Dalle Pezze et al. | 5 | cites it |

## How this is measured

Links are read from each paper's Crossref record as its publisher deposited it: the URLs and repository DOIs in its reference list (the software and data it cites) and its supplementary links (“is-supplemented-by”: its own code or data). A paper that gives its code only in the text, a footnote or a data availability statement is not counted, and some publishers deposit reference lists without their URLs, so every count here is a lower bound. Repository details come from the GitHub REST API (stars, forks, and the repository a fork was made from), the Hugging Face Hub API (likes, downloads, and the base model a model was derived from) and DataCite (Zenodo and other data DOIs, and the GitHub release a Zenodo record archives), read again every month.

## Cite this page

Scholar4AI. (2026). jigsaw-unintended-bias-in-toxicity-classification: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/kaggle-competition-jigsaw-unintended-bias-in-toxicity-classification

```bibtex
@misc{scholar4ai-code-kaggle-competition-jigsaw-unintended-bias-in-toxicity-classification,
  author = {{Scholar4AI}},
  title = {jigsaw-unintended-bias-in-toxicity-classification: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/kaggle-competition-jigsaw-unintended-bias-in-toxicity-classification}},
  urldate = {2026-11-03},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}
```

License: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/).