What Makes a Meme a Meme? Identifying Memes for Memetics-Aware Dataset Creation

TL;DR

This paper proposes a memetics-based protocol to identify genuine memes in datasets, highlighting that many existing datasets contain non-memetic content, which hampers effective meme classification and understanding.

Contribution

It introduces a novel meme identification protocol grounded in memetics and demonstrates its application to existing datasets, revealing significant non-memetic content.

Findings

Over 50% of sampled data lacked memetic content

Existing datasets often contain non-memetic content

A new meme typology based on memetics is proposed

Abstract

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.