Extracting Social Power Relationships from Natural Language.

Sociolinguists have long argued that social context influences language use in all manner of ways, resulting in lects . This paper explores a text classification problem we will call lect modeling , an example of what has been termed computational sociolinguistics. In particular, we use machine learning techniques to identify social power relationships between members of a social network, based purely on the content of their interpersonal communication. We rely on statistical methods, as opposed to language-specific engineering, to extract features which represent vocabulary and grammar usage indicative of social power lect. We then apply support vector machines to model the social power lects representing superior-subordinate communication in the Enron email corpus. Our results validate the treatment of lect modeling as a text classification problem -- albeit a hard one -- and constitute a case for future research in computational sociolinguistics.