Patronizing and Condescending Language Detection in English and Mandarin

A paper submitted to arXiv on 22 October 2026 proposes automated detection of patronizing and condescending language (PCL) in English and Mandarin, reporting that a fine-tuned BERT approach reached an F1 score of 0.528 on the development set, considerably outperforming a baseline using static word embeddings with a convolutional neural network. The authors frame PCL as harmful language that belittles and assumes pity for vulnerable groups, reinforcing negative stereotypes, and note it is often difficult to detect even for human judges. The work targets a detection gap that distinguishes PCL from overt hate speech.

Computer Science Artificial Intelligence Submitted on 22 Oct 2026 Title:Patronizing and Condescending Language Detection in English and Mandarin View PDF https://arxiv.org/pdf/2610.31808 HTML experimental https://arxiv.org/html/2610.31808v1 Abstract:Patronizing and condescending language PCL is a form of harmful language that belittles and assumes pity for vulnerable groups, reinforcing negative societal stereotypes. However, unlike overt hate speech, it is often difficult to detect, even for human judges. This paper proposes a system for the automated detection of PCL. Our first approach uses static word embeddings and a convolutional neural network for classification, while our second approach uses pre-trained BERT embeddings fine-tuned for the PCL task. Our second approach was considerably more successful, and achieved an F1 score of 0.528 over the development set. Additional Features References & Citations Loading... Bibliographic and Citation Tools Bibliographic Explorer What is the Explorer? https://info.arxiv.org/labs/showcase.html arxiv-bibliographic-explorer Connected Papers What is Connected Papers? https://www.connectedpapers.com/about Litmaps What is Litmaps? https://www.litmaps.co/ scite Smart Citations What are Smart Citations? https://www.scite.ai/ Code, Data and Media Associated with this Article alphaXiv What is alphaXiv? https://alphaxiv.org/ CatalyzeX Code Finder for Papers What is CatalyzeX? https://www.catalyzex.com DagsHub What is DagsHub? https://dagshub.com/ Gotit.pub What is GotitPub? http://gotit.pub/faq Hugging Face What is Huggingface? https://huggingface.co/huggingface ScienceCast What is ScienceCast? https://sciencecast.org/welcome Demos Recommenders and Search Tools Influence Flower What are Influence Flowers? https://influencemap.cmlab.dev/ CORE Recommender What is CORE? https://core.ac.uk/services/recommender arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs https://info.arxiv.org/labs/index.html .