# Automatic Discovery of Political Meme Genres with Diverse Appearances

arXiv: https://arxiv.org/abs/2001.06122 · HTML: https://mlsanity.com/paper/2001.06122/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jan 2020.

## Built on by (most cited first, 4 of 4)

- [Motif Mining: Finding and Summarizing Remixed Image Content](https://mlsanity.com/paper/2203.08327/) (2022)
- [More than Memes: A Multimodal Topic Modeling Approach to Conspiracy Theories on Telegram](https://mlsanity.com/paper/2410.08642/) (2024)
- [Clustering Internet Memes Through Template Matching and Multi-Dimensional Similarity](https://mlsanity.com/paper/2505.00056/) (2025)
- [MEWS: Real-time Social Media Manipulation Detection and Analysis](https://mlsanity.com/paper/2205.05783/) (2022)

## Abstract (arXiv)

Forms of human communication are not static -- we expect some evolution in the way information is conveyed over time because of advances in technology. One example of this phenomenon is the image-based meme, which has emerged as a dominant form of political messaging in the past decade. While originally used to spread jokes on social media, memes are now having an outsized impact on public perception of world events. A significant challenge in automatic meme analysis has been the development of a strategy to match memes from within a single genre when the appearances of the images vary. Such variation is especially common in memes exhibiting mimicry. For example, when voters perform a common hand gesture to signal their support for a candidate. In this paper we introduce a scalable automated visual recognition pipeline for discovering political meme genres of diverse appearance. This pipeline can ingest meme images from a social network, apply computer vision-based techniques to extract local features and index new images into a database, and then organize the memes into related genres. To validate this approach, we perform a large case study on the 2019 Indonesian Presidential Election using a new dataset of over two million images collected from Twitter and Instagram. Results show that this approach can discover new meme genres with visually diverse images that share common stylistic elements, paving the way forward for further work in semantic analysis and content attribution.