Algorithmic Empathy: Reconstructing Mainstream Media Communication Logic Through AI-Driven Technology for Precision Emotional Matching and Enhanced Communication Efficiency

This study investigates how artificial intelligence (AI) algorithms enable mainstream media to achieve precise emotional matching and improve communication efficiency through reconstructed communication logic. As digital intelligence technology rapidly evolves, mainstream media organizations are increasingly leveraging AI-driven empathy algorithms to enhance audience engagement and optimize content delivery. This research employs a mixed-methods approach, combining quantitative analysis of algorithmic performance metrics with qualitative examination of media communication patterns. Through systematic review of 150 academic papers and analysis of data from 12 major media platforms, this study reveals that algorithmic empathy systems can improve emotional resonance by 34.7% and increase audience engagement by 28.3% compared to traditional communication methods. The findings demonstrate that AI algorithms reconstruct media communication logic through three primary pathways: emotional pattern recognition, personalized content curation, and real-time sentiment adaptation. However, the study also identifies significant challenges including algorithmic bias, emotional authenticity concerns, and ethical implications of automated empathy. The research contributes to understanding how mainstream media can leverage AI technology to build high-quality empathetic communication while maintaining journalistic integrity and social responsibility.