  Warning: This paper contains memes that may be offensive to some readers.
  Multimodal Internet Memes are now a ubiquitous fixture in online discourse.
One strand of meme-based research is the classification of memes according to
various affects, such as sentiment and hate, supported by manually compiled
meme datasets. Understanding the unique characteristics of memes is crucial for
meme classification. Unlike other user-generated content, memes spread via
memetics, i.e. the process by which memes are imitated and transformed into
symbols used to create new memes. In effect, there exists an ever-evolving pool
of visual and linguistic symbols that underpin meme culture and are crucial to
interpreting the meaning of individual memes. The current approach of training
supervised learning models on static datasets, without taking memetics into
account, limits the depth and accuracy of meme interpretation. We argue that
meme datasets must contain genuine memes, as defined via memetics, so that
effective meme classifiers can be built. In this work, we develop a meme
identification protocol which distinguishes meme from non-memetic content by
recognising the memetics within it. We apply our protocol to random samplings
of the leading 7 meme classification datasets and observe that more than half
(50. 4\%) of the evaluated samples were found to contain no signs of memetics.
Our work also provides a meme typology grounded in memetics, providing the
basis for more effective approaches to the interpretation of memes and the
creation of meme datasets.
