Social Meme-ing: Measuring Linguistic Variation in Memes
Naitian Zhou David Jurgens David Bamman
Much work in the space of NLP has used computational methods to explore
sociolinguistic variation in text. In this paper, we argue that memes, as
multimodal forms of language comprised of visual templates and text, also
exhibit meaningful social variation. We construct a computational pipeline to
cluster individual instances of memes into templates and semantic variables,
taking advantage of their multimodal structure in doing so. We apply this
method to a large collection of meme images from Reddit and make available the
resulting \textsc{SemanticMemes} dataset of 3.8M images clustered by their
semantic function. We use these clusters to analyze linguistic variation in
memes, discovering not only that socially meaningful variation in meme usage
exists between subreddits, but that patterns of meme innovation and
acculturation within these communities align with previous findings on written
language.