mlsanityData to 14 Oct 2026

THOS: A Benchmark Dataset for Targeted Hate and Offensive Speech

arXiv 2311.06446 ↗First posted 11 Nov 2023cs.CL cs.AIcited by 4

On the map: LLM safety and security → Safety alignment, refusal and guardrails

Abstract (arXiv)

Detecting harmful content on social media, such as Twitter, is made difficult by the fact that the seemingly simple yes/no classification conceals a significant amount of complexity. Unfortunately, while several datasets have been collected for training classifiers in hate and offensive speech, there is a scarcity of datasets labeled with a finer granularity of target classes and specific targets. In this paper, we introduce THOS, a dataset of 8.3k tweets manually labeled with fine-grained annotations about the target of the message. We demonstrate that this dataset makes it feasible to train classifiers, based on Large Language Models, to perform classification at this level of granularity.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Placed in Safety alignment, refusal and guardrails, one of 12 directions in LLM safety and security. Builds on 6 of its 19 references that are in the map. First posted on arXiv 11 Nov 2023.

Builds on

6 of 19 references.

References · 6

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  3. 2020BERTweet: A pre-trained language model for English Tweetscited by 346
  4. 2019Predicting the Type and Target of Offensive Posts in Social Mediacited by 271
  5. 2019Multilingual and Multi-Aspect Hate Speech Analysiscited by 133
  6. 2017Latent Multi-task Architecture Learningcited by 29

Built on by

Cited by year

Citing papers per year: 2025: 4.

4 papers.

  1. 2025Guardians and Offenders: A Survey on Harmful Content Generation and Safety Mitigation of LLMcited by 9
  2. 2025Something Just Like TRuST : Toxicity Recognition of Span and Targetcited by 2
  3. 2025AI Text Detectors and the Misclassification of Slightly Polished Arabic Textcited by 1
  4. 2025Towards Generalizable Generic Harmful Speech Datasets for Implicit Hate Speech Detectioncited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2023Revisiting Hate Speech Benchmarks: From Data Curation to System Deployment
  2. 2023Probing LLMs for hate speech detection: strengths and vulnerabilities
  3. 2025Dealing with Annotator Disagreement in Hate Speech Classification
  4. 2024Towards Generalized Offensive Language Identification
  5. 2025HatePrototypes: Interpretable and Transferable Representations for Implicit and Explicit Hate Speech Detection
  6. 2026Enhancing Hate Speech Detection on Social Media: A Comparative Analysis of Machine Learning Models and Text Transformation Approaches
  7. 2024Target Span Detection for Implicit Harmful Content
  8. 2026From Triage to Impact: A Multi-Hop Reasoning Benchmark for Explainable Persian Hate Speech