Automatic Discovery of Political Meme Genres with Diverse Appearances

Authors

  • William Theisen Department of Computer Science and Engineering, University of Notre Dame
  • Joel Brogan Department of Computer Science and Engineering, University of Notre Dame
  • Pamela Bilo Thomas Department of Computer Science and Engineering, University of Notre Dame
  • Daniel Moreira Department of Computer Science and Engineering, University of Notre Dame
  • Pascal Phoa Department of Computer Science and Engineering, University of Notre Dame
  • Tim Weninger Department of Computer Science and Engineering, University of Notre Dame
  • Walter Scheirer Department of Computer Science and Engineering, University of Notre Dame

Keywords:

Trend identification and tracking; time series forecasting, Studies of digital humanities (culture, history, arts) using social media, Organizational and group behavior mediated by social media; interpersonal communication mediated by social media, Qualitative and quantitative studies of social media

Abstract

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.

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Published

2021-05-22

How to Cite

Theisen, W., Brogan, J., Thomas, P. B., Moreira, D., Phoa, P., Weninger, T., & Scheirer, W. (2021). Automatic Discovery of Political Meme Genres with Diverse Appearances. Proceedings of the International AAAI Conference on Web and Social Media, 15(1), 714-726. Retrieved from https://ojs.aaai.org/index.php/ICWSM/article/view/18097