# MemeSequencer: Sparse Matching for Embedding Image Macros

arXiv: https://arxiv.org/abs/1802.04936 · HTML: https://mlsanity.com/paper/1802.04936/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Feb 2018.

## Built on by (most cited first, 10 of 10)

- [On the Evolution of (Hateful) Memes by Means of Multimodal Contrastive Learning](https://mlsanity.com/paper/2212.06573/) (2022)
- [TotalDefMeme: A Multi-Attribute Meme dataset on Total Defence in Singapore](https://mlsanity.com/paper/2305.17911/) (2023)
- [Motif Mining: Finding and Summarizing Remixed Image Content](https://mlsanity.com/paper/2203.08327/) (2022)
- [C-CLIP: Contrastive Image-Text Encoders to Close the Descriptive-Commentative Gap](https://mlsanity.com/paper/2309.03921/) (2023)
- [Dataset and Case Studies for Visual Near-Duplicates Detection in the Context of Social Media](https://mlsanity.com/paper/2203.07167/) (2022)
- [What Makes a Meme a Meme? Identifying Memes for Memetics-Aware Dataset Creation](https://mlsanity.com/paper/2407.11861/) (2024)
- [Needle In A Haystack, Fast: Benchmarking Image Perceptual Similarity Metrics At Scale](https://mlsanity.com/paper/2206.00282/) (2022)
- [N-Modal Contrastive Losses with Applications to Social Media Data in Trimodal Space](https://mlsanity.com/paper/2403.12747/) (2024)
- [Cluster-based Deep Ensemble Learning for Emotion Classification in Internet Memes](https://mlsanity.com/paper/2302.08343/) (2023)
- [Beyond Meme Templates: Limitations of Visual Similarity Measures in Meme Matching](https://mlsanity.com/paper/2508.03562/) (2025)

## Abstract (arXiv)

The analysis of the creation, mutation, and propagation of social media content on the Internet is an essential problem in computational social science, affecting areas ranging from marketing to political mobilization. A first step towards understanding the evolution of images online is the analysis of rapidly modifying and propagating memetic imagery or `memes'. However, a pitfall in proceeding with such an investigation is the current incapability to produce a robust semantic space for such imagery, capable of understanding differences in Image Macros. In this study, we provide a first step in the systematic study of image evolution on the Internet, by proposing an algorithm based on sparse representations and deep learning to decouple various types of content in such images and produce a rich semantic embedding. We demonstrate the benefits of our approach on a variety of tasks pertaining to memes and Image Macros, such as image clustering, image retrieval, topic prediction and virality prediction, surpassing the existing methods on each. In addition to its utility on quantitative tasks, our method opens up the possibility of obtaining the first large-scale understanding of the evolution and propagation of memetic imagery.