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Much work in the space of NLP has used computational methods to exploresociolinguistic variation in text. In this paper, we argue that memes, asmultimodal forms of language comprised of visual templates and text, alsoexhibit meaningful social variation. We construct a computational pipeline tocluster individual instances of memes into templates and semantic variables,taking advantage of their multimodal structure in doing so. We apply thismethod to a large collection of meme images from Reddit and make available theresulting \textsc{SemanticMemes} dataset of 3.8M images clustered by theirsemantic function. We use these clusters to analyze linguistic variation inmemes, discovering not only that socially meaningful variation in meme usageexists between subreddits, but that patterns of meme innovation andacculturation within these communities align with previous findings on writtenlanguage.

Social Meme-ing: Measuring Linguistic Variation in Memes