The Affective Nature of AI-Generated News Images: Impact on Visual Journalism
The Affective Nature of AI-Generated News Images: Impact on Visual Journalism
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DOI：
10.1109/acii59096.2023.10388166
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发表时间：
2023-09
期刊：
影响因子：
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通讯作者：
Sejin Paik;Sarah Bonna;Ekaterina Novozhilova;Ge Gao;Jongin Kim;Derry Wijaya;Margrit Betke
中科院分区：
文献类型：
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作者：
Sejin Paik;Sarah Bonna;Ekaterina Novozhilova;Ge Gao;Jongin Kim;Derry Wijaya;Margrit Betke
本研究探讨了视觉新闻生成人工智能的情感反应和新闻价值感知。虽然生成式人工智能在生成独特图像和削减成本方面为新闻编辑室提供了优势，但人工智能生成的新闻图像的潜在滥用仍令人担忧。在我们的研究中，我们根据新闻道德和摄影指南设计了一个由三部分组成的新闻图像密码本，用于对新闻图像进行情感标记。我们收集了从各种美国新闻来源检索到的有关枪支暴力和气候变化主题的 200 个新闻标题和图像，从 DALL-E 2 生成相应的新闻图像，并询问注释者对遵循密码本的人类选择和人工智能生成的新闻图像的情绪反应。我们还通过测量视觉和文本模态对情绪反应的影响来研究模态对情绪的影响。这项研究的结果提供了对人类和人工智能生成的新闻图像的质量和情感影响的见解。此外，这项工作的结果可有助于制定技术指南以及在新闻制作中道德使用生成式人工智能系统的政策措施。密码本、图像和注释均公开提供，以促进情感计算的未来研究，特别是针对公民和公共利益新闻业。
This study explores the affective responses and newsworthiness perceptions of generative AI for visual journalism. While generative AI offers advantages for newsrooms in terms of producing unique images and cutting costs, the potential misuse of AI-generated news images is a cause for concern. For our study, we designed a 3-part news image codebook for affect-labeling news images based on journalism ethics and photography guidelines. We collected 200 news headlines and images retrieved from a variety of U.S. news sources on the topics of gun violence and climate change, generated corresponding news images from DALL-E 2 and asked annotators their emotional responses to the human-selected and AI-generated news images following the codebook. We also examined the impact of modality on emotions by measuring the effects of visual and textual modalities on emotional responses. The findings of this study provide insights into the quality and emotional impact of generative news images produced by humans and AI. Further, results of this work can be useful in developing technical guidelines as well as policy measures for the ethical use of generative AI systems in journalistic production. The codebook, images and annotations are made publicly available to facilitate future research in affective computing, specifically tailored to civic and public-interest journalism.