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Same Story, Different Lens: A Multi-Agent System for Cross-Outlet News Framing Analysis
Affan Yasin, Yueqi Lyu, Rubia Fatima, Weinchen Zhong, Keyi Li

Same Story, Different Lens: A Multi-Agent System for Cross-Outlet News Framing Analysis

Computer Science & Mathematics Published 2026 Verification Pending

Overview & Synopsis

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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Indexed from repository: arXiv (arxiv.org)
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Document Information

Document Title Same Story, Different Lens: A Multi-Agent System for Cross-Outlet News Framing Analysis
Author / Organization Affan Yasin, Yueqi Lyu, Rubia Fatima, Weinchen Zhong, Keyi Li
Subject & Discipline cs.AI
Categories Computer Science & Mathematics
Publication Year 2026
License / Rights arXiv Open Access / Creative Commons
Resource Verification Listed PDF (Verification Pending)
Original Host Website arXiv (arxiv.org)
File Format application/pdf (Portable Document Format)
Language EN
Catalog ID DOC-3521
Original Source Link Visit Source Page on arxiv.org »