# What Makes a Meme a Meme? Identifying Memes for Memetics-Aware Dataset Creation

arXiv: https://arxiv.org/abs/2407.11861 · HTML: https://mlsanity.com/paper/2407.11861/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 39 references that are in the map.
- First posted on arXiv 16 Jul 2024.

## Builds on (most cited first, 12 of 12)

- [The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes](https://mlsanity.com/paper/2005.04790/) (2020)
- [SemEval-2020 Task 8: Memotion Analysis -- The Visuo-Lingual Metaphor!](https://mlsanity.com/paper/2008.03781/) (2020)
- [Detecting and Understanding Harmful Memes: A Survey](https://mlsanity.com/paper/2205.04274/) (2022)
- [On the Origins of Memes by Means of Fringe Web Communities](https://mlsanity.com/paper/1805.12512/) (2018)
- [Detecting Propaganda Techniques in Memes](https://mlsanity.com/paper/2109.08013/) (2021)
- [Hate Speech in Pixels: Detection of Offensive Memes towards Automatic Moderation](https://mlsanity.com/paper/1910.02334/) (2019)
- [On the Evolution of (Hateful) Memes by Means of Multimodal Contrastive Learning](https://mlsanity.com/paper/2212.06573/) (2022)
- [AOMD: An Analogy-aware Approach to Offensive Meme Detection on Social Media](https://mlsanity.com/paper/2106.11229/) (2021)
- [TotalDefMeme: A Multi-Attribute Meme dataset on Total Defence in Singapore](https://mlsanity.com/paper/2305.17911/) (2023)
- [MemeSequencer: Sparse Matching for Embedding Image Macros](https://mlsanity.com/paper/1802.04936/) (2018)
- [Supervised Contrastive Learning for Affect Modelling](https://mlsanity.com/paper/2208.12238/) (2022)
- [Unimodal Intermediate Training for Multimodal Meme Sentiment Classification](https://mlsanity.com/paper/2308.00528/) (2023)

## Built on by (most cited first, 2 of 2)

- [Large Vision-Language Models for Knowledge-Grounded Data Annotation of Memes](https://mlsanity.com/paper/2501.13851/) (2025)
- [Beyond Meme Templates: Limitations of Visual Similarity Measures in Meme Matching](https://mlsanity.com/paper/2508.03562/) (2025)

## Abstract (arXiv)

Warning: This paper contains memes that may be offensive to some readers. Multimodal Internet Memes are now a ubiquitous fixture in online discourse. One strand of meme-based research is the classification of memes according to various affects, such as sentiment and hate, supported by manually compiled meme datasets. Understanding the unique characteristics of memes is crucial for meme classification. Unlike other user-generated content, memes spread via memetics, i.e. the process by which memes are imitated and transformed into symbols used to create new memes. In effect, there exists an ever-evolving pool of visual and linguistic symbols that underpin meme culture and are crucial to interpreting the meaning of individual memes. The current approach of training supervised learning models on static datasets, without taking memetics into account, limits the depth and accuracy of meme interpretation. We argue that meme datasets must contain genuine memes, as defined via memetics, so that effective meme classifiers can be built. In this work, we develop a meme identification protocol which distinguishes meme from non-memetic content by recognising the memetics within it. We apply our protocol to random samplings of the leading 7 meme classification datasets and observe that more than half (50. 4%) of the evaluated samples were found to contain no signs of memetics. Our work also provides a meme typology grounded in memetics, providing the basis for more effective approaches to the interpretation of memes and the creation of meme datasets.