Computer Vision and Internet Meme Genealogy: An Evaluation of Image Feature Matching as a Technique for Pattern Detection

Abstract

ABSTRACT Internet memes are a fundamental aspect of digital culture. Despite being individual expressions, they vastly transcend the individual level as windows into and vehicles for wide-stretching social, cultural, and political narratives. Empirical research into meme culture is thriving, yet particularly compartmentalized. In the humanities and social sciences, most efforts involve in-depth linguistic and visual analyses of mostly handpicked examples of memes, begging the question on the origins and meanings of those particular expressions. In technical disciplines, such as computer science, efforts are focused on the large-scale identification and classification of meme images, as well as patterns of “viral” spread at scale. This contribution aims to bridge the chasm between depth and scale by introducing a three-step approach suitable for “computational grounded theoretical” studies in which (1) an automated procedure establishes formal links between meme images drawn from a large-scale corpus paving the way for (2) network analysis to infer patterns of relatedness and spread, and (3) practically classifying visually related images in file folders for the purpose of further local, hermeneutic analysis. The procedure is demonstrated and evaluated on two datasets: an artificially constructed, structured dataset and a naturally harvested unstructured dataset. Future horizons and domains of application are discussed.