用大模型同时分析新闻图文，揭示媒体偏见背后的隐含框架。
Multi-Modal Framing Analysis of News
- 结合图文内容，用大模型识别新闻中的多重隐含框架。
- 发现视觉元素与文字常传递不一致的框架，揭示编辑选择的影响。
- 适合研究媒体偏见、政治传播的学者和数据新闻从业者。
自动化政治传播中的框架分析是计算社会科学研究的热门任务，用于探究作者如何选择话题的特定方面以塑造受众认知。现有研究多局限于预定义的固定框架，且仅关注文本，忽略了文本所处的视觉语境，尤其在新闻报道中，这遗漏了版面设计、配图等编辑决策的重要信息。为克服此局限，我们提出一种基于大规模（视觉-）语言模型的多模态、多标签框架分析方法，可大规模执行。基于框架理论，我们提取图像中隐含的意义，并与文本框架对比，揭示图文间的差异。此外，通过针对特定议题的框架分析，识别出高度党派化的表达模式，这些模式在以往定性研究中已有发现。本方法实现了对新闻中文本与图像的整合式、可扩展的框架分析，为理解媒体偏见提供了更完整的视角。
原文摘要 · Abstract (English)
Automated frame analysis of political communication is a popular task in computational social science that is used to study how authors select aspects of a topic to frame its reception. So far, such studies have been narrow, in that they use a fixed set of pre-defined frames and focus only on the text, ignoring the visual contexts in which those texts appear. Especially for framing in the news, this leaves out valuable information about editorial choices, which include not just the written article but also accompanying photographs. To overcome such limitations, we present a method for conducting multi-modal, multi-label framing analysis at scale using large (vision-) language models. Grounding our work in framing theory, we extract latent meaning embedded in images used to convey a certain point and contrast that to the text by comparing the respective frames used. We also identify highly partisan framing of topics with issue-specific frame analysis found in prior qualitative work. We demonstrate a method for doing scalable integrative framing analysis of both text and image in news, providing a more complete picture for understanding media bias.
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