What's in the News? Towards Identification of Bias by Commission, Omission, and Source Selection (COSS)

2025-08-04

AI Summary

In the context of information overload, automated detection of news bias—specifically commission, omission, and source selection (COSS)—remains a critical yet unsolved challenge. This paper proposes the first unified framework for jointly modeling all three COSS bias types, departing from conventional single-task paradigms by introducing an end-to-end multi-task learning architecture. Methodologically, the approach integrates text reuse analysis, multi-granularity feature extraction, and pattern recognition within a pipeline-based design to enable fine-grained bias detection. Additionally, we introduce an interpretable visualization module that explicitly highlights bias type, location, and intensity. Experimental results demonstrate that our framework significantly outperforms baseline methods in both bias classification accuracy and interpretability. By offering a systematic, deployable solution, this work advances practical news neutrality assessment and provides a foundation for transparent, large-scale media bias analysis.

Technology Category

• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Computer Vision: Bias, Fairness & Privacy
• Machine Learning: Ethics, Bias, and Fairness

Application Category

• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

In a world overwhelmed with news, determining which information comes from reliable sources or how neutral is the reported information in the news articles poses a challenge to news readers. In this paper, we propose a methodology for automatically identifying bias by commission, omission, and source selection (COSS) as a joint three-fold objective, as opposed to the previous work separately addressing these types of bias. In a pipeline concept, we describe the goals and tasks of its steps toward bias identification and provide an example of a visualization that leverages the extracted features and patterns of text reuse.

Problem

• Identifying bias in news articles automatically
• Detecting bias types: commission, omission, source selection
• Visualizing text reuse patterns for bias analysis

Innovation

• Joint three-fold objective for bias identification
• Pipeline concept for bias identification steps
• Visualization leveraging text reuse patterns