What Media Frames Reveal About Stance: A Dataset and Study about Memes in Climate Change Discourse
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 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 frame detection. We evaluate 7B LLaVA-NeXT and Molmo in various setups, and report the corresponding results on their LLM backbone. On both tasks, we observe models exhibiting strong in-context learning capabilities. Human captions consistently enhance performance. Synthetic captions and human-corrected OCR also help occasionally. Our findings highlight that VLMs perform well on stance, but struggle on frames, where LLMs outperform VLMs. Finally, we perform a case study on memes reflecting sociological concepts of climate change, analyzing VLMs’ limitations in handling nuanced frames and stance expressions in this domain.
Paper Type: Long
Research Area: Resources and Evaluation
Research Area Keywords: meme, climate change, stance detection, frame detection, communication science
Contribution Types: Model analysis & interpretability, Data resources, Data analysis
Languages Studied: English
Submission Number: 7196
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