Who Judges the Frame? Auditing Multimodal LLM Judges for News Framing Across Event-Level Perspectives

Large Language Models (LLMs), including multimodal variants, are increasingly utilized for analyzing news framing, sentiment, and perspective in media. However, their use as measurement instruments presents a significant methodological challenge, as their outputs may be influenced by model-specific tendencies, prompt design, and inherent socio-cultural or linguistic assumptions, rather than solely reflecting content properties.

The increasing reliance on AI models for sophisticated data analysis, particularly in areas like media content and public discourse, necessitates robust validation. Understanding and mitigating the inherent biases and limitations of these analytical tools is crucial for ensuring the reliability and integrity of insights derived from large-scale data processing, impacting decision-making in various sectors.

News coverage of significant global events is influenced by both the reported content and the framing conveyed through text, images, and their combination.

Forward consideration, not a verified fact.