基于图像生成的新闻立场检测的视觉框架
Visual Framing for News Stance Detection via Image Generation
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中文总结 AI 辅助
研究针对新闻立场检测的隐含性挑战，提出VFStance方法，通过图像生成的视觉框架强化立场线索，实验及用户研究验证其有效性与潜在扩展应用。
中文摘要 AI 辅助
文章级新闻立场检测旨在识别新闻文章对社会议题的观点。尽管立场检测已取得进展且对可信媒体环境至关重要，但新闻文章存在独特挑战：其立场往往隐含，通过新闻框架微妙传达，且嵌入冗长、结构复杂的文本中。为应对这些挑战，我们提出VFStance，该方法利用视觉框架，通过图像生成使隐含的立场线索更明确。在评估实验中，我们证明VFStance优于现有方法，且视觉框架对其性能有贡献。最后，在基于片段的新闻消费场景中开展的受控用户研究（N=200）进一步表明，VFStance可使立场信号在视觉上更显著，并凸显其在自动立场检测之外的潜在应用。
英文摘要
Article-level news stance detection aims to identify the perspective of news articles toward social issues. Despite advances in stance detection and its importance for trustworthy media environments, news articles pose distinct challenges because their stances are often implicit, subtly conveyed through journalistic framing, and embedded in long, structurally complex texts. To address these challenges, we introduce VFStance, which leverages visual framing to make implicit stance cues more explicit via image generation. In evaluation experiments, we demonstrate the effectiveness of VFStance over existing methods and the contribution of visual framing to its performance. Finally, a controlled user study (N=200) in a snippet-based news consumption setting further demonstrates that VFStance can make stance signals visually salient and highlights its potential use beyond automated stance detection.
发表机构
- Soongsil University（崇实大学）
- KAIST（韩国科学技术院）
机构由 AI 辅助整理，请以论文原文为准。
