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.
View on arXiv@article{prasse2025_2503.09361,
title={ Deep Learning for Climate Action: Computer Vision Analysis of Visual Narratives on X },
author={ Katharina Prasse and Marcel Kleinmann and Inken Adam and Kerstin Beckersjuergen and Andreas Edte and Jona Frroku and Timotheus Gumpp and Steffen Jung and Isaac Bravo and Stefanie Walter and Margret Keuper },
journal={arXiv preprint arXiv:2503.09361},
year={ 2025 }
}