Same Story, Different Lens: A Multi-Agent System for Cross-Outlet News Framing Analysis
Affan Yasin · Yueqi Lyu · Rubia Fatima · Weinchen Zhong · Keyi Li
报道同一事件的新闻媒体，往往强调或忽略的内容各不相同。《Same Story, Different Lens》是一个实验性研究原型，用于比较这些报道选择。该系统收集17家媒体的世界与政治报道，将同一事件的相关报道分组，并从情感、政治倾向、叙事框架、带偏见语言、可能遗漏内容，以及模型估算的事实性主张与观点比例等方面生成结构化分析。带偏见语言条目包括原文引述和中性改写。三个模型角色（智能体）分别负责文章分析、来源支持度评估和跨媒体简报生成。所有分析存入JSONL档案。评估将媒体层面的倾向估计与近似的预设参考评级比较，并汇总Critic模型分配的依据支持分数。这些指标仅提供初步诊断。依据支持分数反映模型对所提供文本中支持关系的判断，不能证明事实准确性，也不构成独立验证。
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arXiv:2610.35208v1 Announce Type: new Abstract: News outlets covering the same event differ in what they emphasize and omit. \textit{Same Story, Different Lens} is an experimental research prototype for comparing these choices. It collects world and politics coverage from 17 outlets, groups reports about the same event, and produces structured analyses of sentiment, political lean, framing, loaded language, possible omissions, and model-estimated proportions of factual claims and opinion. Loaded-language entries include quotations and neutral rewordings. Three model roles (agents) handle article analysis, assessment of source support, and cross-outlet briefing generation. Analyses are stored in a JSONL archive. The evaluation compares outlet-level lean estimates with approximate, predefined reference ratings and summarizes the Critic's model-assigned grounding scores. These measures provide preliminary diagnostics. A grounding score reflects the model's assessment of support in the supplied text and does not establish factual accuracy or constitute independent verification.
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