Characterizing English Variation across Social Media Communities with BERT
arXiv:2102.06820
Abstract
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.
arXiv comment18 pages, 5 figures, accepted to TACL 2021, please cite that version
References in corpus (7)
- Scikit-learn: Machine Learning in Python
- Characterizing the Google Books corpus: Strong limits to inferences of socio-cultural and linguistic evolution
- Diffusion of Lexical Change in Social Media
- Divergent discourse between protests and counter-protests: #BlackLivesMatter and #AllLivesMatter
- Analysing Lexical Semantic Change with Contextualised Word Representations
- Niche as a determinant of word fate in online groups
- Towards better substitution-based word sense induction