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.

翻译：自然语言处理领域的大量研究已使用计算方法探索文本中的社会语言变异。本文提出，迷因作为由视觉模板与文本组成的多模态语言形式，同样展现有意义的社交变异。我们构建了一条计算流水线，利用迷因的多模态结构，将单个迷因实例聚类为模板与语义变量。我们将该方法应用于Reddit上大规模迷因图像集合，生成并公开了按语义功能聚类的3.8M图像数据集\textsc{SemanticMemes}。利用这些聚类结果分析迷因中的语言变异，我们不仅发现不同子论坛间迷因使用存在具社会意义的变异，还观察到这些社群内迷因创新与文化适应的模式与先前书面语言研究的发现相吻合。