印地语印度政治新闻媒体偏见数据集发布，448 篇文章由 14 位专家标注

View PDF

Abstract:Media bias understanding and detection remains an under-researched problem for Indian context, specifically for Indic languages, such as, Hindi, which is widely spoken in India. In this paper, we introduce a comprehensive, multi-annotator, explainable dataset consisting of 448 unique articles from four leading Indian news agencies publishing in Hindi. We focus on Hindi, the most spoken and an official language of India. The study of media bias is particularly relevant in the Indian context due to the country's linguistic diversity and large population, where the same event is often reported by multiple news agencies in different languages, thereby introducing scope for bias. The dataset comprises Hindi political news articles annotated by 14 experts and classified into four bias categories - selection bias, framing bias, clickbait bias and omission bias - when an article is identified as biased or unbiased. For each biased article, annotators also provide a textual explanation, enabling future semi-automated approaches to incorporate explainability when language models reason over the dataset. Cohen's Kappa scores were 0.81 for simple bias/no-bias classification reflecting strong inter-annotator agreement. For the multi-label task, the Cohen's Kappa values are 0.89, 0.75, 0.81, and 0.79 for clickbait, selection, framing, and omission biases, respectively, resulting in an average Kappa of 0.80, which also reflects strong agreement.

Comments:	17 pages, 3 figures, 13 tables
Subjects:	Computation and Language (cs.CL)
Cite as:	arXiv:2611.13686 [cs.CL]
	(or arXiv:2611.13686v1 [cs.CL] for this version)
	https://doi.org/10.48550/arXiv.2611.13686 arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Kushal Trivedi [view email]
[v1] Tue, 10 Nov 2026 07:34:05 UTC (238 KB)