Through the looking glass: what NLP can reveal about sociolinguistic variation
People adapt their language use in different social contexts to meet communicative needs: a person may use the word <going> with colleagues and <goin'> with their close friends. Sociolinguistics researchers investigate the systematic variation in language use across different contexts to determine the social meaning of variation, such as how people change their word choices for different audiences. While traditional sociolinguistics investigates variation in spoken language, computational sociolinguistics relies on natural language processing and statistical methods to investigate written language in online discussions. This talk will explore how NLP can help isolate sociolinguistic phenomena that would otherwise go understudied in spoken contexts, and more broadly how NLP can help social science research.
Ian Stewart is a Visiting Research Investigator with the LIT Lab at the University of Michigan. Ian recently defended his thesis in the Human-Centered Computing PhD program at the Georgia Institute of Technology, and his research proposed computational approaches to understanding sociolinguistic variation on social media. Ian is currently interested in incorporating sociolinguistic insight into NLP models to address different speaker needs.