Decoding News Bias: Multi Bias Detection in News Articles
Bhushan Santosh Shah, Deven Santosh Shah, Vahida Attar

TL;DR
This paper explores the detection of multiple biases across diverse news domains using large language models, creating a dataset and applying various detection techniques to improve news integrity.
Contribution
It introduces a comprehensive approach to detect multiple biases in news articles across various domains using LLMs and new dataset construction.
Findings
Built a large bias detection dataset using LLMs
Applied multiple bias detection techniques to news articles
Highlighted the importance of broad-spectrum bias detection
Abstract
News Articles provides crucial information about various events happening in the society but they unfortunately come with different kind of biases. These biases can significantly distort public opinion and trust in the media, making it essential to develop techniques to detect and address them. Previous works have majorly worked towards identifying biases in particular domains e.g., Political, gender biases. However, more comprehensive studies are needed to detect biases across diverse domains. Large language models (LLMs) offer a powerful way to analyze and understand natural language, making them ideal for constructing datasets and detecting these biases. In this work, we have explored various biases present in the news articles, built a dataset using LLMs and present results obtained using multiple detection techniques. Our approach highlights the importance of broad-spectrum bias…
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Taxonomy
TopicsMisinformation and Its Impacts · Topic Modeling · Hate Speech and Cyberbullying Detection
