This is the companion site for the paper:
Demographic Dialectal Variation in Social Media: A
Case Study of African-American English.
Su Lin Blodgett,
Forthcoming, Proceedings of EMNLP 2016. [pdf]
Though dialectal language is increasingly abundant on social media, few resources exist for developing NLP tools to handle such language. We conduct a case study of dialectal language in online conversational text by investigating African-American English (AAE) on Twitter. We propose a distantly supervised model to identify AAE-like language from demographics associated with geo-located messages, and we verify that this language follows well-known AAE linguistic phenomena. In addition, we analyze the quality of existing language identification and dependency parsing tools on AAE-like text, demonstrating that they perform poorly on such text compared to text associated with white speakers. We also provide an ensemble classifier for language identification which eliminates this disparity and release a new corpus of tweets containing AAE-like language.
These datasets are made available for research purposes only.
If you use them in research, please cite the paper.