Computational Social Roles

Millions of people participate in online communities, exchange expertise and ideas, and collaborate to produce complex artifacts. They often enact a variety of social roles in the process of helping their communities and the public at large, which strongly influence the amount and types of work they do, and how they coordinate their activities. Better understanding members’ roles benefits members by clarifying how they should behave to participate effectively and also benefits the community overall by encouraging members to contribute in ways that best use their skills and interests. 

Social sciences have provided rich theoretic taxonomies of social roles within groups, while natural language processing techniques enable us to automate the identification of social roles in online communities. However, most social science work has focused on generic roles without accommodating the activities associated with tasks in specific contexts or automating the process of role identification. While there has been work to date about automatic role inference, identification of social roles has not had a corresponding strong emphasis in the language technologies community. A variety of methods were developed to extract specific “roles” or patterns in different contexts, lacking generalized definitions about what are roles and systematic methods to extract roles. Moreover, how roles change over time and how the awareness of roles influence role holders’ performance and the group production, have not been adequately researched in both fields. 

This thesis advocates for both theories of social science and models of text analysis to better define roles, develop ways to extract roles and optimally recommend roles to users. Concretely, this work defines what are social roles, introduces five measurable facets associated with social roles, and proposed a generic methodology for role identification. It also demonstrated how to computationally model social role and its facets in two socially important contexts - Wikipedia and Cancer Survivor Network. Via combining theories of social roles and computational models for role identification on those two large-scale contexts, this research reveals details about emergent, behavioral, and functioning roles, and a set of computational techniques to identify such roles via fine-grained operationalization of role holders’ behaviors. This work fills the longstanding gap in role theory and empirical modeling about emergent roles in online communities, and lays the foundation for future work to identify and analyze roles that people enacted in group processes both online and offline.