Social Meme-ing: Measuring Linguistic Variation in Memes

Naitian Zhou¹, David Jurgens² and David Bamman¹

¹ University of California, Berkeley ² University of Michigan

📝 Read the Paper 💻 View the Code 📊 Get the data

Introduction

Much work in the space of NLP has used computational methods to explore sociolinguistic variation in text. In this paper, we argue that memes, as multimodal forms of language comprised of visual templates and text, also exhibit meaningful social variation.

Highlights

We learn the semantics of meme templates without supervision. We take advantage of the multimodal structure to learn how fill text aligns with the template by fine-tuning a RoBERTa model, giving semantic embeddings for templates which we can then cluster.

We create the SemanticMemes dataset. We use this method to construct and make available a dataset of 3.8M Reddit memes grouped into semantically coherent clusters.

We find memes with socially meaningful variation. Not only do subreddits prefer certain variants of a template over others, but they choose templates that index into a localized cultural knowledge, making cultural allusions to characters or celebrities. Click below to see examples.

"I prefer to K"

Memes are multimodal constructions where the base image template and additional text fills both have semantic value.

Different subreddits systematically prefer some meme templates over other semantically equivalent ones. **Click on a cluster** to see the different variants. All examples here are statistically significantly overrepresented in their respective subreddits, p < 0.05.

What's more, we find that patterns of linguistic innovation and acculturation that have been previously observed in text also occur with memes! Read the paper for more details about these experiments, as well as our experiments with using CLIP to train a multimodal model and more information about the evaluation process.

Explore the data

You can download the dataset with Reddit post ID, image URL, RoBERTa semantic cluster label, and template visual cluster label at this link (63MB gzipped).

Use the dropdown below to view some more examples of semantic clusters.

Visually diverse clusters emerge even for complex semantic functions.

Conclusion

We hope that this work will encourage more research into the social language of memes, and that the SemanticMemes dataset will be a useful resource for future work.

To cite this work:



@misc{zhou2023social,
      title={Social Meme-ing: Measuring Linguistic Variation in Memes}, 
      author={Naitian Zhou and David Jurgens and David Bamman},
      year={2023},
      eprint={2311.09130},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}