mlsanityData to 14 Oct 2026

Ruddit: Norms of Offensiveness for English Reddit Comments

arXiv 2106.05664 ↗First posted 10 Jun 2021cs.CL cs.AIcited by 27

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Abstract (arXiv)

On social media platforms, hateful and offensive language negatively impact the mental well-being of users and the participation of people from diverse backgrounds. Automatic methods to detect offensive language have largely relied on datasets with categorical labels. However, comments can vary in their degree of offensiveness. We create the first dataset of English language Reddit comments that has fine-grained, real-valued scores between -1 (maximally supportive) and 1 (maximally offensive). The dataset was annotated using Best--Worst Scaling, a form of comparative annotation that has been shown to alleviate known biases of using rating scales. We show that the method produces highly reliable offensiveness scores. Finally, we evaluate the ability of widely-used neural models to predict offensiveness scores on this new dataset.

Key facts. Cited by 27 papers in the map (data through 14 Oct 2026). Builds on 20 of its 69 references that are in the map. First posted on arXiv 10 Jun 2021.

Builds on

20 of 69 references.

References · 20

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2018Model Cards for Model Reportingcited by 1,080
  3. 2017Automated Hate Speech Detection and the Problem of Offensive Languagecited by 736
  4. 2019Social Bias Frames: Reasoning about Social and Power Implications of Languagecited by 380
  5. 2020The Pushshift Reddit Datasetcited by 292
  6. 2018Large Scale Crowdsourcing and Characterization of Twitter Abusive Behaviorcited by 270
  7. 2016Ex Machina: Personal Attacks Seen at Scalecited by 210
  8. 2019Racial Bias in Hate Speech and Abusive Language Detection Datasetscited by 209
  9. 2019SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval)cited by 207
  10. 2020HateBERT: Retraining BERT for Abusive Language Detection in Englishcited by 207
  11. 2018Reducing Gender Bias in Abusive Language Detectioncited by 152
  12. 2019Emotion Recognition in Conversation: Research Challenges, Datasets, and Recent Advancescited by 119
8 more
  1. 2017Understanding Abuse: A Typology of Abusive Language Detection Subtaskscited by 116
  2. 2017Best-Worst Scaling More Reliable than Rating Scales: A Case Study on Sentiment Intensity Annotationcited by 79
  3. 2017Detecting Online Hate Speech Using Context Aware Modelscited by 72
  4. 2019Tackling Online Abuse: A Survey of Automated Abuse Detection Methodscited by 33
  5. 2017Emotion Intensities in Tweetscited by 32
  6. 2017Capturing Reliable Fine-Grained Sentiment Associations by Crowdsourcing and Best-Worst Scalingcited by 23
  7. 2018Transfer Learning from LDA to BiLSTM-CNN for Offensive Language Detection in Twittercited by 6
  8. 2020Towards Ethics by Design in Online Abusive Content Detectioncited by 6

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Citing papers per year: 2021: 2; 2022: 3; 2023: 6; 2024: 8; 2025: 5; 2026: 3.

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  4. 2021Just Say No: Analyzing the Stance of Neural Dialogue Generation in Offensive Contextscited by 56
  5. 2023When Do Annotator Demographics Matter? Measuring the Influence of Annotator Demographics with the POPQUORN Datasetcited by 52
  6. 2024HarmAug: Effective Data Augmentation for Knowledge Distillation of Safety Guard Modelscited by 19
  7. 2025A Personalized Conversational Benchmark: Towards Simulating Personalized Conversationscited by 17
  8. 2023''Fifty Shades of Bias'': Normative Ratings of Gender Bias in GPT Generated English Textcited by 15
  9. 2023A Benchmark for Understanding Dialogue Safety in Mental Health Supportcited by 13
  10. 2024Which Demographics do LLMs Default to During Annotation?cited by 10
  11. 2024The Unseen Targets of Hate -- A Systematic Review of Hateful Communication Datasetscited by 6
  12. 2025Evaluating how LLM annotations represent diverse views on contentious topicscited by 6
15 more
  1. 2022CRUSH: Contextually Regularized and User anchored Self-supervised Hate speech Detectioncited by 5
  2. 2023On the rise of fear speech in online social mediacited by 5
  3. 2022Which one is more toxic? Findings from Jigsaw Rate Severity of Toxic Commentscited by 4
  4. 2025QGuard:Question-based Zero-shot Guard for Multi-modal LLM Safetycited by 4
  5. 2021WLV-RIT at GermEval 2021: Multitask Learning with Transformers to Detect Toxic, Engaging, and Fact-Claiming Commentscited by 3
  6. 2024"You are an expert annotator": Automatic Best-Worst-Scaling Annotations for Emotion Intensity Modelingcited by 3
  7. 2025SafeRoute: Adaptive Model Selection for Efficient and Accurate Safety Guardrails in Large Language Modelscited by 3
  8. 2025Protect: Towards Robust Guardrailing Stack for Trustworthy Enterprise LLM Systemscited by 3
  9. 2022Analyzing the Intensity of Complaints on Social Mediacited by 0
  10. 2023Style Locality for Controllable Generation with kNN Language Modelscited by 0
  11. 2024Ultra Low-Cost Two-Stage Multimodal System for Non-Normative Behavior Detectioncited by 0
  12. 2024PoPreRo: A New Dataset for Popularity Prediction of Romanian Reddit Postscited by 0
  13. 2026Cross-Lingual Probing and Community-Grounded Analysis of Gender Bias in Low-Resource Bengalicited by 0
  14. 2026GASTON: Graph-Aware Social Transformer for Online Networkscited by 0
  15. 2026Demographic Prompting at Scale: When More Attributes Hurt LLM--Human Agreementcited by 0