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.

通过构建计算机管道以研究多模态的字符与视觉元素构成的表情包，发现在不同社区之间存在有意义的表情包使用的社会差异，且这些社区内的表情包创新与文化适应模式与书面语言的先前研究结果相吻合。

社交梗文化：衡量梗中的语言变异

Social Meme-ing: Measuring Linguistic Variation in Memes

There have been recent advances in computer-based recognition of isolated,
citation-form signs from video. There are many challenges for such a task, not
least the naturally occurring inter- and intra- signer synchronic variation in
sign production, including sociolinguistic variation in the realization of
certain signs. However, there are several significant factors that make
recognition of signs from continuous signing an even more difficult problem.
This article presents an overview of such challenges, based in part on findings
from a large corpus of linguistically annotated video data for American Sign
Language (ASL). Some linguistic regularities in the structure of signs that can
boost handshape and sign recognition are also discussed.

电脑识别视频中孤立符号存在着一些困难，其中包括手语者之间的口头和非口头的同步变化以及特定符号的实现中存在的社会语言学变化。本文概述了这些挑战，并基于大规模的语言学注释视频数据的发现，对美国手语的符号结构中的某些规律进行了讨论。