Automatic Discovery of Meme Genres with Diverse Appearances
Appeared In: Proceedings of the International AAAI Conference on Web and Social Media
Publication Date: June 2021
This paper introduces a scalable automated visual recognition pipeline for discovering 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, the authors 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.
"Automatic Discovery of Political Meme Genres with Diverse Appearances," William Theisen, Joel Brogan, Pamela Bilo Thomas, Daniel Moreira, Pascal Phoa, Tim Weninger, Walter J. Scheirer, Proceedings of the International AAAI Conference on Web and Social Media (ICWSM), June 2021.