What Media Frames Reveal About Stance: A Dataset and Study about Memes in Climate Change Discourse (2025)

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 frame detection. We evaluate LLaVA-NeXT and Molmo in various setups, and report the corresponding results on their LLM backbone. 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 analyze VLMs’ limitations in handling nuanced frames and stance expressions on climate change internet memes.

## introduction internet memes are a powerful communication format in online discourse that reflect communities' 1 we make our dataset publicly available at https:// github.com/mainlp/climatememes . cultural and social dynamics @xcite . multimodal digital items combine images and texts to convey complex viewpoints in a compact and engaging format @xcite . through these multimodal expressions, communicators convey their positions towards particular topics, i.e., stances as defined by @xcite . while stance reflects the creator's opinion toward a target, the specific narrative used to convey a certain stance is shaped by media frames. media framing refers to selecting specific aspects of a perceived reality in communication to portray how an issue is defined, how its causes are interpreted, how its moral implications are evaluated, and what potential solutions are considered @xcite . depending on their stance, creators may gravitate toward different framing strategies @xcite . however, the interaction between stance and media frames remains under-studied, particularly in their representation through humorous social media content such as memes. this is especially relevant for debates of global significance, such as climate change. memes about climate change (cc) are widespread on social media, including twitter/x @xcite . for example, figure 1a conveys a convinced stance towards cc by using real and impact frames (further detailed in §3.2) to affirm the evidence of global warming and its disheartening consequences. conversely, figure 1b conveys a skeptical stance using the hoax frame, claiming that cc is not a major issue or even not real, and suggests that politics may distort the cc issue. in this paper, we analyze stances and media frames in cc memes by examining the following three research questions (rqs): ## climatememes dataset this section describes climatememes, a dataset of 1,184 cc memes from 47 subreddits annotated with media frames and stances. figure 2 illustrates our data processing pipeline. we discuss meme collection and climate filtering ( §3.1), and present guidelines for stance and frame annotations ( §3.2). we also provide manual ocr correction and meme caption annotation for future uptake ( §3.3). ## stance and media frame detection to what degree can vlms detect stance and frames in a meme, and how can we improve their performance? this section reports various experiments we performed on climatememes. 5.1 experimental setups models we evaluate two open-source vlms on multimodal memes: llava-v1.6-mistral-7b (llava, liu et al. 2024) and molmo-7b-d (molmo, deitke et al. 2024), both of a visual @xmath0. to compare, we experiment with text-only inputs on their llm backbones: mistral-7b (mistral, jiang et al. 2023) and qwen2-7b (qwen, yang et al. 2024). ## meme understanding through the lens of communication science to understand which aspects challenge models in meme understanding, we apply an interdisciplinary approach, integrating three concepts from communication science research: humor type, personalization, and responsibility. these concepts are critical in understanding the construction of meaning in affective climate communication on social media and may pose challenges for detection tasks. humor is a key feature of memes as they are usually created with the intention to entertain people @xcite . thus, we analyzed different humor types, such as puns, sarcasm, and surprise, which can have varying effects on readers. personalization is a common communication strategy in political communication in general, and it simplifies complex political issues by focusing on individual actors instead of groups. we recruited two bachelor's students in communication science to annotate humor type, person, and responsibility on 235 climatememes test memes. our guidelines are adapted from a comprehensive codebook on "climate change and social media" foot_3 provided by @xcite , allowing multiple labels per item (detailed definitions in appendix g). in stance detection, llava performs well on memes with humor types exaggeration and personification. memes with pun and surprise are difficult, receiving the lowest f1 score. memes with politicial and ordinary under personalization categories show strong performance, but ngo stands out as challenging. for responsibility, memes concerning the macro-level are the easiest for the model, while meso-level memes are the hardest. since micro/meso-level responsibilities address specific individuals (e.g., politicians) or organizations (e.g., companies), we hypothesize that macro-level responsibility (e.g., the society) leads to less variation and eases meme understanding. in frame detection, memes with humor types pun and joke are the hardest. under personalization, memes featuring ngo or celebrities are easier for the model than political ones. frame detection on memes attributing responsibility mirrors results on stance detection, with macro performing best and meso worst. in sum, this interdisciplinary annotation using taxonomies from communication science provides insights into aspects that caused difficulties in meme stance and frame detection. appendix h provides additional error analyses. ## conclusion we introduce climatememes, a new benchmark dataset of climate change memes annotated with stance and media frames. we demonstrate that media frame preferences are strong indicators of stance, with convinced and skeptical stances favoring distinct frames. we compare vlms and llms and identify challenges in understanding multimodal information. our paper also integrates concepts from communication science and reveals which meme aspects challenged the model.