@inproceedings{panda-etal-2026-indregbias,
title = "{I}nd{R}eg{B}ias: A Dataset for Studying {I}ndian Regional Biases in {E}nglish and Code-Mixed Social Media Comments",
author = "Panda, Debasmita and
Anil, Akash and
Shukla, Neelesh Kumar",
editor = "Xiong, Deyi and
Branco, Ant{\'o}nio and
Zhang, Hongming and
Dong, Yue and
Wang, Benyou and
Zhang, Wenxuan and
Zhou, Li and
Zheng, Jingting",
booktitle = "Proceedings of the First Workshop on Pluralistic Value Alignment of {LLM}s",
month = nov,
year = "2026",
address = "Hengqin, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.plurvallm-1.4/",
pages = "43--62",
ISBN = "979-8-89176-453-8",
abstract = "While social biases corresponding to gender, race, socio-economic conditions etc., have been extensively studied in the major applications of Natural Language Processing (NLP), biases corresponding to regions have garnered less attention. This is mainly because of (i) difficulty in extraction of regional biased dataset, (ii) disagreements in annotation due to inherent human{'}s biases, and (iii) regional biases are studied in combination with other types of social biases and are often under-represented. This paper focuses on creating a dataset IndRegBias, consisting of regional biases in an Indian context reflected by user{'}s comments on popular social media platforms, namely Reddit and YouTube. We carefully selected 25000 comments appearing on various threads in Reddit and videos on YouTube discussing trending topics on regional issues in India. Furthermore, we propose a multilevel annotation strategy to annotate the comments describing the severity of regional biased statements. To detect the presence of regional bias and their severity in IndRegBias, we evaluate open source Large Language Models (LLM) and Indic Language Models (ILM) by using Zero-Shot, Few-Shot, and Fine-Tuning strategies. We observe that Zero-Shot and Few-Shot approaches show lower accuracy in detecting regional biases and severity in majority of the LLMs and ILMs. However, Fine-Tuning approach significantly enhances the performance of the LLM in detecting Indian regional bias along with their severity."
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<abstract>While social biases corresponding to gender, race, socio-economic conditions etc., have been extensively studied in the major applications of Natural Language Processing (NLP), biases corresponding to regions have garnered less attention. This is mainly because of (i) difficulty in extraction of regional biased dataset, (ii) disagreements in annotation due to inherent human’s biases, and (iii) regional biases are studied in combination with other types of social biases and are often under-represented. This paper focuses on creating a dataset IndRegBias, consisting of regional biases in an Indian context reflected by user’s comments on popular social media platforms, namely Reddit and YouTube. We carefully selected 25000 comments appearing on various threads in Reddit and videos on YouTube discussing trending topics on regional issues in India. Furthermore, we propose a multilevel annotation strategy to annotate the comments describing the severity of regional biased statements. To detect the presence of regional bias and their severity in IndRegBias, we evaluate open source Large Language Models (LLM) and Indic Language Models (ILM) by using Zero-Shot, Few-Shot, and Fine-Tuning strategies. We observe that Zero-Shot and Few-Shot approaches show lower accuracy in detecting regional biases and severity in majority of the LLMs and ILMs. However, Fine-Tuning approach significantly enhances the performance of the LLM in detecting Indian regional bias along with their severity.</abstract>
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%0 Conference Proceedings
%T IndRegBias: A Dataset for Studying Indian Regional Biases in English and Code-Mixed Social Media Comments
%A Panda, Debasmita
%A Anil, Akash
%A Shukla, Neelesh Kumar
%Y Xiong, Deyi
%Y Branco, António
%Y Zhang, Hongming
%Y Dong, Yue
%Y Wang, Benyou
%Y Zhang, Wenxuan
%Y Zhou, Li
%Y Zheng, Jingting
%S Proceedings of the First Workshop on Pluralistic Value Alignment of LLMs
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, China
%@ 979-8-89176-453-8
%F panda-etal-2026-indregbias
%X While social biases corresponding to gender, race, socio-economic conditions etc., have been extensively studied in the major applications of Natural Language Processing (NLP), biases corresponding to regions have garnered less attention. This is mainly because of (i) difficulty in extraction of regional biased dataset, (ii) disagreements in annotation due to inherent human’s biases, and (iii) regional biases are studied in combination with other types of social biases and are often under-represented. This paper focuses on creating a dataset IndRegBias, consisting of regional biases in an Indian context reflected by user’s comments on popular social media platforms, namely Reddit and YouTube. We carefully selected 25000 comments appearing on various threads in Reddit and videos on YouTube discussing trending topics on regional issues in India. Furthermore, we propose a multilevel annotation strategy to annotate the comments describing the severity of regional biased statements. To detect the presence of regional bias and their severity in IndRegBias, we evaluate open source Large Language Models (LLM) and Indic Language Models (ILM) by using Zero-Shot, Few-Shot, and Fine-Tuning strategies. We observe that Zero-Shot and Few-Shot approaches show lower accuracy in detecting regional biases and severity in majority of the LLMs and ILMs. However, Fine-Tuning approach significantly enhances the performance of the LLM in detecting Indian regional bias along with their severity.
%U https://aclanthology.org/2026.plurvallm-1.4/
%P 43-62
Markdown (Informal)
[IndRegBias: A Dataset for Studying Indian Regional Biases in English and Code-Mixed Social Media Comments](https://aclanthology.org/2026.plurvallm-1.4/) (Panda et al., PlurVA-LLM 2026)
ACL