  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.
