Geopolitical Parallax: Beyond Walter Lippmann Just After Large Language Models

TL;DR

This paper examines how large language models from Chinese and Western origins differ in news content evaluation, revealing persistent geopolitical biases that impact perceived news quality and subjectivity, emphasizing the need for cultural calibration in LLM applications.

Contribution

It introduces a systematic comparison of Chinese and Western LLMs in news quality assessment, highlighting model-origin biases and their implications for media evaluation.

Findings

Western models score higher on subjectivity and positive emotion in Palestine coverage.

Chinese models emphasize novelty and descriptiveness, with lower fluency and technicality scores.

Bias patterns align with media bias theory and differ across geopolitical topics.

Abstract

Objectivity in journalism has long been contested, oscillating between ideals of neutral, fact-based reporting and the inevitability of subjective framing. With the advent of large language models (LLMs), these tensions are now mediated by algorithmic systems whose training data and design choices may themselves embed cultural or ideological biases. This study investigates geopolitical parallax-systematic divergence in news quality and subjectivity assessments-by comparing article-level embeddings from Chinese-origin (Qwen, BGE, Jina) and Western-origin (Snowflake, Granite) model families. We evaluate both on a human-annotated news quality benchmark spanning fifteen stylistic, informational, and affective dimensions, and on parallel corpora covering politically sensitive topics, including Palestine and reciprocal China-United States coverage. Using logistic regression probes and matched-topic evaluation, we quantify per-metric differences in predicted positive-class probabilities between model families. Our findings reveal consistent, non-random divergences aligned with model origin. In Palestine-related coverage, Western models assign higher subjectivity and positive emotion scores, while Chinese models emphasize novelty and descriptiveness. Cross-topic analysis shows asymmetries in structural quality metrics Chinese-on-US scoring notably lower in fluency, conciseness, technicality, and overall quality-contrasted by higher negative emotion scores. These patterns align with media bias theory and our distinction between semantic, emotional, and relational subjectivity, and extend LLM bias literature by showing that geopolitical framing effects persist in downstream quality assessment tasks. We conclude that LLM-based media evaluation pipelines require cultural calibration to avoid conflating content differences with model-induced bias.