用智能代理分析新闻段落立场，提升长文观点识别准确率。
Journalism-Guided Agentic In-Context Learning for News Stance Detection
- 设计新闻导向的智能体，在上下文学习中逐段判断立场。
- 在2000篇韩文新闻上达到领先性能，段落级标注达21650条。
- 适合关注媒体偏见分析与多元观点推荐的研究者使用。
随着在线新闻消费的增长，个性化推荐系统已成为数字新闻的重要组成部分。然而，这些系统因未能纳入多元视角，可能加剧信息茧房和政治极化。立场检测——识别文本对目标议题的立场——有助于缓解此问题，实现观点感知的推荐和媒体偏见的数据驱动分析。但现有研究多局限于短文本和高资源语言。为此，我们提出首个韩文文章级立场检测数据集 extsc{K-News-Stance}，包含2000篇新闻文章，涵盖47个社会议题，具有文章级及21650条段落级标注。我们还提出 extsc{JoA-ICL} 框架，即基于新闻学指导的智能体上下文学习方法，通过语言模型智能体预测关键结构段落（如导语、引语）的立场，并聚合推断整体文章立场。实验表明， extsc{JoA-ICL} 超越现有方法，凸显段落级智能体在捕捉长文整体立场中的优势。两个案例研究进一步验证其在促进观点多样性推荐和揭示媒体偏见模式方面的潜力。
原文摘要 · Abstract (English)
As online news consumption grows, personalized recommendation systems have become integral to digital journalism. However, these systems risk reinforcing filter bubbles and political polarization by failing to incorporate diverse perspectives. Stance detection -- identifying a text's position on a target -- can help mitigate this by enabling viewpoint-aware recommendations and data-driven analyses of media bias. Yet, existing stance detection research remains largely limited to short texts and high-resource languages. To address these gaps, we introduce \textsc{K-News-Stance}, the first Korean dataset for article-level stance detection, comprising 2,000 news articles with article-level and 21,650 segment-level stance annotations across 47 societal issues. We also propose \textsc{JoA-ICL}, a \textbf{Jo}urnalism-guided \textbf{A}gentic \textbf{I}n-\textbf{C}ontext \textbf{L}earning framework that employs a language model agent to predict the stances of key structural segments (e.g., leads, quotations), which are then aggregated to infer the overall article stance. Experiments showed that \textsc{JoA-ICL} outperforms existing stance detection methods, highlighting the benefits of segment-level agency in capturing the overall position of long-form news articles. Two case studies further demonstrate its broader utility in promoting viewpoint diversity in news recommendations and uncovering patterns of media bias.
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