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

Autores/as

  • Guangnan Zhu Digital Media Research Centre, Queensland University of Technology, Australia
  • Kunal Chand Queensland University of Technology
  • Daniel Angus Queensland University of Technology
  • Timothy Graham Queensland University of Technology

DOI:

https://doi.org/10.5210/spir.v2023i0.13520

Palabras clave:

meme machine, machine vision, meme classification, social media

Resumen

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.

Descargas

Publicado

2023-12-31

Cómo citar

Zhu, . G., Chand, K., Angus, D., & Graham, T. (2023). MEMES, MULTIMODALITIES, AND MACHINES: ASSEMBLING MULTIMODAL PATTERNS IN MEME CLASSIFICATION STUDY. AoIR Selected Papers of Internet Research. https://doi.org/10.5210/spir.v2023i0.13520

Número

Sección

Papers Z