“You’re Mr. Lebowski, I’m the Dude”: Inducing Address Term Formality in Signed Social Networks

Human Language Technologies: The 2015 Annual Conference of the North American Chapter of the ACL, pages 1616–1626, Denver, Colorado, May 31 – June 5, 2015. c⃝2015 Association for Computational Linguistics “You’re Mr. Lebowski, I’m the Dude”: Inducing Address Term Formality in Signed Social Networks Vinodh Krishnan College of Computing Georgia Institute of Technology Atlanta, GA 30308 krishnan.vinodh@gmail.com Jacob Eisenstein School of Interactive Computing Georgia Institute of Technology Atlanta, GA 30308 jacobe@gatech.edu Abstract We present an unsupervised model for induc- ing signed social networks from the content exchanged across network edges. Inference in this model solves three problems simulta- neously: (1) identifying the sign of each edge; (2) characterizing the distribution over content for each edge type; (3) estimating weights for triadic features that map to theoretical mod- els such as structural balance. We apply this model to the problem of inducing the social function of address terms, such as Madame, comrade, and dude. On a dataset of movie scripts, our system obtains a coherent cluster- ing of address terms, while at the same time making intuitively plausible judgments of the formality of social relations in each film. As an additional contribution, we provide a boot- strapping technique for identifying and tag- ging address terms in dialogue.1 1 Introduction One of the core communicative functions of lan- guage is to modulate and reproduce social dynam- ics, such as friendship, familiarity, formality, and power (Hymes, 1972). However, large-scale em- pirical work on understanding this communicative function has been stymied by a lack of labeled data: it is not clear what to annotate, let alone whether and how such annotations can be produced reliably. Computational linguistics has made great progress in modeling language’s informational dimension, 1Code and data for this paper is available at https:// github.com/vinodhkris/signed-social. but — with a few notable exceptions — computa- tion has had little to contribute to our understanding of language’s social dimension. Yet there is a rich theoretical literature on social structures and dynamics. In this paper, we focus on one such structure: signed social networks, in which edges between individuals are annotated with information about the nature of the relationship. For example, the individuals in a dyad may be friends or foes; they may be on formal or informal terms; or they may be in an asymmetric power relation- ship. Several theories characterize signed social net- works: in structural balance theory, edge signs in- dicate friendship and enmity, with some triads of signed edges being stable, and others being unsta- ble (Cartwright and Harary, 1956); conversely, in status theory (Leskovec et al., 2010b), edges indicate status differentials, and triads should obey transitiv- ity. But these theoretical models can only be applied when the sign of each social network connection is known, and they do not answer the sociolinguistic question of how the sign of a social tie relates to the language that is exchanged across it. We present a unified statistical model that incor- porates both network structure and linguistic con- tent. The model connects signed …

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