用视觉分析破解社交媒体气候叙事，揭示图文情感差异。
Deep Learning for Climate Action: Computer Vision Analysis of Visual Narratives on X
- 融合图像识别与情感分析，解析73万条推文中的视觉叙事
- 发现图片与文字在气候议题上存在显著情感分歧
- 开源工具支持后续气候传播研究，适合社科与计算交叉学者
气候变化是21世纪最紧迫的挑战之一，引发社交媒体上的广泛讨论。在后API时代，社会媒体数据获取日益受限，而活动家、政策制定者和研究人员亟需理解公众情绪与话语趋势。本研究分析了2019年来自X（前称Twitter）的73万条气候相关推文及其附带图像。方法结合统计分析、图像分类、目标检测与情感分析，探索气候话题中的视觉叙事。我们还开发了一个图形用户界面（GUI），支持交互式数据探索。结果揭示了气候传播中的关键主题，凸显图像与文本间的情感差异，并评估了基础模型在社交媒体图像分析中的优势与局限。通过开源代码与工具，我们旨在推动气候变迁、社交媒体与计算机视觉交叉领域的后续研究。
原文摘要 · Abstract (English)
Climate change is one of the most pressing challenges of the 21st century, sparking widespread discourse across social media platforms. Activists, policymakers, and researchers seek to understand public sentiment and narratives while access to social media data has become increasingly restricted in the post-API era. In this study, we analyze a dataset of climate change-related tweets from X (formerly Twitter) shared in 2019, containing 730k tweets along with the shared images. Our approach integrates statistical analysis, image classification, object detection, and sentiment analysis to explore visual narratives in climate discourse. Additionally, we introduce a graphical user interface (GUI) to facilitate interactive data exploration. Our findings reveal key themes in climate communication, highlight sentiment divergence between images and text, and underscore the strengths and limitations of foundation models in analyzing social media imagery. By releasing our code and tools, we aim to support future research on the intersection of climate change, social media, and computer vision.
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