M eta M eme: A Dataset for Meme Template and Meta-Category Classification (2025)

Abstract: This paper introduces a new dataset for classifying memes by their template and communicative intent.It includes a broad selection of meme templates and examples scraped from imgflip and a smaller hand-annotated set of memes scraped from Reddit.The Reddit memes have been annotated for meta-category using a novel annotation scheme that classifies memes by the structure of the perspective they are being used to communicate.YOLOv11 and ChatGPT 4o are used to provide baseline modeling results.We find that YOLO struggles with template classification on real-world data but outperforms ChatGPT in classifying meta-categories.

## introduction and related work the majority of the previous research on the automatic classification of memes revolves around specific domains like the detection of politics or hate speech or classifying memes by humor "type" (courtois and frissen, 2023). little work addresses task of identifying which template-not necessary a single literal image, but a recognizable reference format for the meme-a meme falls into, but courtois and frissen offer one of the most promising recent attempts to do so. their work uses two datasets: a dataset of randomly selected memes and hand-annotated templates from one of the most widely used meme documentation websites, knowyourmeme, and a dataset of memes paired with their templates scraped from the aggregator website 9gag. their method for template identification involved cnns for detecting features common across examples, calculating accuracy with a geometric mean given that parts of their datasets had templates not seen in training. along similar lines, gleason et al. (2019) attempt simple meme template detection. they use a combination of the the multi-scale structural similarity (ms-ssim) index and a color histogram between the input and template image (wang et al., 2003) to match memes to their templates. even with their small dataset of 385 memes and 137 templates, they achieved an accuracy of 92.25%. this paper offers a similar methodology for obtaining the dataset to courtois and frissen (2023), but with a larger and more varied set of templates. while gleason et al. (2019) provides promising results for a small dataset, we expect our template identification task to be much more difficult, with over 2,000 template classes compared to their 137. additionally, gleason et al. (2019) likely reached near-ceiling performance because all of their memes were generated using the exact same source images for templates, but the reality of meme usage across the internet is that there is more room for variation, as they are often not made using generators with consistent template images. along with examples from imgflip (a meme generator website), we also include memes sourced from reddit. in total, our dataset covers 2,059 meme templates with a collection of 274,748 examples scraped from imgflip and a collection of 242 hand-annotated memes from reddit. with the inclusion of social media-obtained memes, we expose our model to examples of a given template that are likely much more diverse than those found exclusively on generator websites, which gives a better window into performance on real-world data. to support the annotation of the reddit memes, we introduce an ontology of meta-categories meant to explore the communicative intent behind memes. recent work has used llms and vl (vision language) models to identify the metaphors represented by a given meme, as well as explaining the entire joke itself (hwang and shwartz, 2023). as an alternative to this, we explore a structure-based approach with the assumption that an understanding of the desired intent in using a meme can be identified without requiring any context. between the meta-category classification task and the more traditional meme template classification task, this dataset contributions a novel resource for the study of meme creation and usage. 1 2 dataset 2.1 data sources ## future work and conclusion while the chatgpt results were disappointing, the consistency of the poor results could suggest that the model might need something more complex than just prompts. for example, using chain-ofthought or reinforcement learning might help the model improve perform performance by allowing it to reason more. for the same reason, chatgpt's o1 pro model, which now has vision capabilities, might show stronger results as well (noda et al., 2025). as for the yolo models, it suggests that the meta-category ontology can be predicted, with reasonable accuracy, using a pure computer vision the best way to improve the performance of both tasks is likely to be getting more data. scraping from more sources than imgflip and reddit would provide a more diverse dataset, as well as being able to handle more meme formats, like gifs. the reddit dataset was also very small due to the limited amount of annotator time available, and more data would likely have improved the performance of our models. on a similar note, in the imgflip dataset, there were meme templates that were extremely similar to each other, as discussed further in the appendix. while we removed exact duplicates in data preprocessing, there are likely still meme template classes that could be combined, and would likely improve the performance of the yolo model on meme template classification.