# Don't Patronize Me! An Annotated Dataset with Patronizing and Condescending Language towards Vulnerable Communities

arXiv: https://arxiv.org/abs/2011.08320 · HTML: https://mlsanity.com/paper/2011.08320/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Nov 2020.

## Built on by (most cited first, 19 of 19)

- [Text Detoxification using Large Pre-trained Neural Models](https://mlsanity.com/paper/2109.08914/) (2021)
- [Facilitating Fine-grained Detection of Chinese Toxic Language: Hierarchical Taxonomy, Resources, and Benchmarks](https://mlsanity.com/paper/2305.04446/) (2023)
- [Language Models for Text Classification: Is In-Context Learning Enough?](https://mlsanity.com/paper/2403.17661/) (2024)
- [From Dogwhistles to Bullhorns: Unveiling Coded Rhetoric with Language Models](https://mlsanity.com/paper/2305.17174/) (2023)
- [Predicting Different Types of Subtle Toxicity in Unhealthy Online Conversations](https://mlsanity.com/paper/2106.03952/) (2021)
- [BEIKE NLP at SemEval-2022 Task 4: Prompt-Based Paragraph Classification for Patronizing and Condescending Language Detection](https://mlsanity.com/paper/2208.01312/) (2022)
- [The crime of being poor](https://mlsanity.com/paper/2303.14128/) (2023)
- [ML_LTU at SemEval-2022 Task 4: T5 Towards Identifying Patronizing and Condescending Language](https://mlsanity.com/paper/2204.07432/) (2022)
- [Xu at SemEval-2022 Task 4: Pre-BERT Neural Network Methods vs Post-BERT RoBERTa Approach for Patronizing and Condescending Language Detection](https://mlsanity.com/paper/2211.06874/) (2022)
- [PALI-NLP at SemEval-2022 Task 4: Discriminative Fine-tuning of Transformers for Patronizing and Condescending Language Detection](https://mlsanity.com/paper/2203.04616/) (2022)
- [Towards Patronizing and Condescending Language in Chinese Videos: A Multimodal Dataset and Detector](https://mlsanity.com/paper/2409.05005/) (2024)
- [AfriWOZ: Corpus for Exploiting Cross-Lingual Transferability for Generation of Dialogues in Low-Resource, African Languages](https://mlsanity.com/paper/2204.08083/) (2022)
- [PclGPT: A Large Language Model for Patronizing and Condescending Language Detection](https://mlsanity.com/paper/2410.00361/) (2024)
- [SATLab at SemEval-2022 Task 4: Trying to Detect Patronizing and Condescending Language with only Character and Word N-grams](https://mlsanity.com/paper/2203.05355/) (2022)
- [UTSA NLP at SemEval-2022 Task 4: An Exploration of Simple Ensembles of Transformers, Convolutional, and Recurrent Neural Networks](https://mlsanity.com/paper/2203.14920/) (2022)
- [UMass PCL at SemEval-2022 Task 4: Pre-trained Language Model Ensembles for Detecting Patronizing and Condescending Language](https://mlsanity.com/paper/2204.08304/) (2022)
- [Taygete at SemEval-2022 Task 4: RoBERTa based models for detecting Patronising and Condescending Language](https://mlsanity.com/paper/2204.10519/) (2022)
- [CPCLDETECTOR: Knowledge Enhancement and Alignment Selection for Chinese Patronizing and Condescending Language Detection](https://mlsanity.com/paper/2509.18562/) (2025)
- [Benevolent Bias in Multi-Turn Human-Agent Dialogue](https://mlsanity.com/paper/2608.29206/) (2026)

## Abstract (arXiv)

In this paper, we introduce a new annotated dataset which is aimed at supporting the development of NLP models to identify and categorize language that is patronizing or condescending towards vulnerable communities (e.g. refugees, homeless people, poor families). While the prevalence of such language in the general media has long been shown to have harmful effects, it differs from other types of harmful language, in that it is generally used unconsciously and with good intentions. We furthermore believe that the often subtle nature of patronizing and condescending language (PCL) presents an interesting technical challenge for the NLP community. Our analysis of the proposed dataset shows that identifying PCL is hard for standard NLP models, with language models such as BERT achieving the best results.