Much previous work characterizing language variation across Internet social
groups has focused on the types of words used by these groups. We extend this
type of study by employing BERT to characterize variation in the senses of
words as well, analyzing two months of English comments in 474 Reddit
communities. The specificity of different sense clusters to a community,
combined with the specificity of a community's unique word types, is used to
identify cases where a social group's language deviates from the norm. We
validate our metrics using user-created glossaries and draw on sociolinguistic
theories to connect language variation with trends in community behavior. We
find that communities with highly distinctive language are medium-sized, and
their loyal and highly engaged users interact in dense networks.

使用 BERT 对 474 个英文 Reddit 社群的两个月评论进行分析，将社群独特的词语类型与不同词义群集的特异性结合起来，识别社群语言偏离常态的情况，并运用社会语言学理论将语言变异与社群行为趋势相联系，发现具有高度独特语言的社群规模适中，其忠诚和高度参与的用户在密集网络中互动。

利用 BERT 表征社交媒体社群中的英语变化

Characterizing English Variation across Social Media Communities with  BERT

Stress is a nigh-universal human experience, particularly in the online
world. While stress can be a motivator, too much stress is associated with many
negative health outcomes, making its identification useful across a range of
domains. However, existing computational research typically only studies stress
in domains such as speech, or in short genres such as Twitter. We present
Dreaddit, a new text corpus of lengthy multi-domain social media data for the
identification of stress. Our dataset consists of 190K posts from five
different categories of Reddit communities; we additionally label 3.5K total
segments taken from 3K posts using Amazon Mechanical Turk. We present
preliminary supervised learning methods for identifying stress, both neural and
traditional, and analyze the complexity and diversity of the data and
characteristics of each category.

这篇论文介绍了一份新的文本语料库 Dreaddit，该语料库包含来自五个不同类别 Reddit 社群的 19 万条帖子，用于识别长篇社交媒体数据中的压力表征，并提出了基于监督学习的方法。