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

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

← Code and data repositories cited in research papers

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.

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#

20232025

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	1
Cognitive Computation	1
Language Resources and Evaluation	1
Expert Systems with Applications	1

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	Machine Learning	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	Language Resources and Evaluation	Nikolay Babakov, Varvara Logacheva, Alexander Panchenko	8	cites it
2025-02-01	Discovering the Cognitive Bias of Toxic Language Through Metaphorical Concept Mappings	Cognitive Computation	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	Expert Systems with Applications	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

@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}
}

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