MEMES, MULTIMODALITIES, AND MACHINES: ASSEMBLING MULTIMODAL PATTERNS IN MEME CLASSIFICATION STUDY

Abstract

Memes are an important facet of current online communication on social media, with rich social, political, and cultural significance and power. This present work focuses on developing computational frameworks to support textual and visual content analysis of online memes, assisting the profiling of the unique contents and interrelationships of different meme characteristics. The framework focusses on decomposing the multimodal subcomponents of online memes to support accurate sorting and classification of meme exploitable and other rich textual materials. We showcase the development of a multimodal meme classification toolbox with the capability to utilize more abundant information from those multimodal components, with a view towards bolstering and extending existing meme analysis methods for cultural and media studies.