Decoding News Bias: Multi Bias Detection in News Articles

2025-01-05

AI Summary

This study addresses the automatic identification of multi-dimensional biases—such as political and gender bias—in news texts. We introduce MultiBias-News, the first large-scale, LLM-synthesized dataset covering a broad spectrum of bias types. Methodologically, we propose the first unified modeling framework for fine-grained, cross-domain bias detection; innovatively leveraging LLaMA and GPT to generate high-quality synthetic annotations, and integrating fine-tuned BERT, zero-shot classification, and ensemble learning for robust bias detection. Experiments on MultiBias-News demonstrate that our best-performing model achieves an F1 score of 86.3%, substantially outperforming single-bias baselines. This work overcomes the limitations of conventional single-type bias detection approaches, establishing a scalable, empirically grounded technical foundation for assessing media fairness and enhancing public trust in news content.

Technology Category

• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Machine Learning: Ethics, Bias, and Fairness
• Computer Vision: Bias, Fairness & Privacy

Application Category

• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

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 detection and offers new insights for improving the integrity of news articles.

Problem

• Bias Detection
• Media Objectivity
• Public Trust

Innovation

• Large Language Models
• Comprehensive Bias Detection
• News Impartiality