mlsanityData to 14 Oct 2026

Which one is more toxic? Findings from Jigsaw Rate Severity of Toxic Comments

arXiv 2206.13284 ↗First posted 27 Jun 2022cs.CLcited by 4

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Abstract (arXiv)

The proliferation of online hate speech has necessitated the creation of algorithms which can detect toxicity. Most of the past research focuses on this detection as a classification task, but assigning an absolute toxicity label is often tricky. Hence, few of the past works transform the same task into a regression. This paper shows the comparative evaluation of different transformers and traditional machine learning models on a recently released toxicity severity measurement dataset by Jigsaw. We further demonstrate the issues with the model predictions using explainability analysis.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 21 of its 30 references that are in the map. First posted on arXiv 27 Jun 2022.

Builds on

21 of 30 references.

References · 21

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9 more
  1. 2020Measuring a hate speech spectrum with faceted Rasch item response theory and perspective-aware, explainable-by-design deep learningcited by 68
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  3. 2021Ruddit: Norms of Offensiveness for English Reddit Commentscited by 27
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  5. 2021"Short is the Road that Leads from Fear to Hate": Fear Speech in Indian WhatsApp Groupscited by 13
  6. 2021You too Brutus! Trapping Hateful Users in Social Media: Challenges, Solutions & Insightscited by 11
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Built on by

Cited by year

Citing papers per year: 2023: 2; 2024: 1; 2025: 1.

4 papers.

  1. 2023Levels of AGI for Operationalizing Progress on the Path to AGIcited by 96
  2. 2024Towards Building Specialized Generalist AI with System 1 and System 2 Fusioncited by 8
  3. 2023Toxic language detection: a systematic review of Arabic datasetscited by 3
  4. 2025AdaPRL: Adaptive Pairwise Regression Learning with Uncertainty Estimation for Universal Regression Taskscited by 0