A Domain-Adaptive Soft Prompting Framework for Multi-Type Bias Detection in News

Abstract

Bias in news content represents a significant challenge for information systems and media analysis. This paper presents a domain-adaptive soft prompting framework for detecting multiple types of bias in news content using advanced natural language processing techniques.

Our research addresses the challenges of:

• Multi-Type Bias Detection: Identifying various forms of bias including political, gender, racial, and ideological bias
• Domain Adaptation: Adapting detection models to different news sources and contexts
• Soft Prompting: Using flexible prompting techniques for improved detection accuracy
• Real-time Analysis: Providing timely bias detection for news content

The framework demonstrates superior performance in bias detection across multiple domains while maintaining interpretability and explainability.

Key Contributions

1. Multi-Type Framework: Development of comprehensive bias detection across multiple categories
2. Domain Adaptation: Techniques for adapting to different news sources and contexts
3. Soft Prompting: Innovative prompting strategies for improved detection
4. Interpretable Results: Providing explainable bias detection outcomes

Research Impact

This work contributes to the development of fair and unbiased information systems and provides tools for media organizations to monitor and address bias in their content.