Exploring patterns of identity usage in tweets

Sociologists have long been interested in the ways that iden-tities, or labels for people, are created, used and applied across various social contexts. The present work makes two contributions to the study of identity, in particular the study of identity in text. We first consider the following novel NLP task: given a set of text data (here, from Twitter), label each word in the text as being representative of a (possibly multi-word) identity. To address this task, we develop a comprehensive feature set that leverages several avenues of recent NLP work on Twitter and use these features to train a supervised classiffier. Our model outperforms a surprisingly strong rule-based baseline by 33%. We then use our model for a case study, applying it to a large corpora of Twitter data from users who actively discussed the Eric Garner and Michael Brown cases. Among other findings, we observe that the identities used by individuals differ in interesting ways based on social context measures derived from census data.