# TalkDown: A Corpus for Condescension Detection in Context

arXiv: https://arxiv.org/abs/1909.11272 · HTML: https://mlsanity.com/paper/1909.11272/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Sep 2019.

## Built on by (most cited first, 21 of 21)

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- [Do LLMs Understand Social Knowledge? Evaluating the Sociability of Large Language Models with SocKET Benchmark](https://mlsanity.com/paper/2305.14938/) (2023)
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- [Predicting Different Types of Subtle Toxicity in Unhealthy Online Conversations](https://mlsanity.com/paper/2106.03952/) (2021)
- [Quantifying Gender Biases Towards Politicians on Reddit](https://mlsanity.com/paper/2112.12014/) (2021)
- [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)
- [TalkUp: Paving the Way for Understanding Empowering Language](https://mlsanity.com/paper/2305.14326/) (2023)
- [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)
- [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)
- ["Yeah Right!" -- Do LLMs Exhibit Multimodal Feature Transfer?](https://mlsanity.com/paper/2501.04138/) (2025)
- [CPCLDETECTOR: Knowledge Enhancement and Alignment Selection for Chinese Patronizing and Condescending Language Detection](https://mlsanity.com/paper/2509.18562/) (2025)

## Abstract (arXiv)

Condescending language use is caustic; it can bring dialogues to an end and bifurcate communities. Thus, systems for condescension detection could have a large positive impact. A challenge here is that condescension is often impossible to detect from isolated utterances, as it depends on the discourse and social context. To address this, we present TalkDown, a new labeled dataset of condescending linguistic acts in context. We show that extending a language-only model with representations of the discourse improves performance, and we motivate techniques for dealing with the low rates of condescension overall. We also use our model to estimate condescension rates in various online communities and relate these differences to differing community norms.