What Media Frames Reveal About Stance: A Dataset and Study about Memes in Climate Change Discourse
Shijia Zhou, Siyao Peng, Simon M. Luebke, J\"org Ha{\ss}ler, Mario Haim, Saif M. Mohammad, Barbara Plank

TL;DR
This paper introduces CLIMATEMEMES, a new dataset of climate change memes annotated with stance and media frames, and explores how multimodal models detect these aspects, revealing strengths and limitations of current AI in understanding meme-based climate discourse.
Contribution
The work presents the first dataset of climate memes with stance and frame annotations and evaluates multimodal models on stance and frame detection tasks.
Findings
VLMs perform well on stance detection but struggle with framing.
LLMs outperform VLMs in media frame detection.
Human captions improve model performance.
Abstract
Media framing refers to the emphasis on specific aspects of perceived reality to shape how an issue is defined and understood. Its primary purpose is to shape public perceptions often in alignment with the authors' opinions and stances. However, the interaction between stance and media frame remains largely unexplored. In this work, we apply an interdisciplinary approach to conceptualize and computationally explore this interaction with internet memes on climate change. We curate CLIMATEMEMES, the first dataset of climate-change memes annotated with both stance and media frames, inspired by research in communication science. CLIMATEMEMES includes 1,184 memes sourced from 47 subreddits, enabling analysis of frame prominence over time and communities, and sheds light on the framing preferences of different stance holders. We propose two meme understanding tasks: stance detection and media…
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