  Memes are a modern form of communication and meme templates possess a base
semantics that is customizable by whomever posts it on social media. Machine
learning systems struggle with memes, which is likely due to such systems
having insufficient context to understand memes, as there is more to memes than
the obvious image and text. Here, to aid understanding of memes, we release a
knowledge base of memes and information found on www.knowyourmeme.com, which we
call the Know Your Meme Knowledge Base (KYMKB), composed of more than 54,000
images. The KYMKB includes popular meme templates, examples of each template,
and detailed information about the template. We hypothesize that meme templates
can be used to inject models with the context missing from previous approaches.
To test our hypothesis, we create a non-parametric majority-based classifier,
which we call Template-Label Counter (TLC). We find TLC more effective than or
competitive with fine-tuned baselines. To demonstrate the power of meme
templates and the value of both our knowledge base and method, we conduct
thorough classification experiments and exploratory data analysis in the
context of five meme analysis tasks.
