Wednesday, January 24, 2018 - 3:30am to 5:30am
Location:6501 Gates & Hillman Centers
For More Information, Contact:Stacey Young, firstname.lastname@example.org
The LTI is proud to announce the following PhD Thesis Proposal:
Computational Social Roles: Identify, Recommend and Configure Emergent Social Roles in Online Communities
Robert Kraut, (Co-Chair)
Eduard Hovy, (Co-Chair)
Brandy L. Aven
Dan Jurafsky, (Stanford University)
Millions of people participate in online communities, exchange expertise and ideas, and collaborate to produce complex artifacts, such as Wikipedia. They engage in a variety of roles, 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. However, most social science work has focused on generic roles without accommodating the activities associated with tasks in specific contexts. Although a variety of methods were developed to extract specific “roles” and patterns in different contexts, generalized definitions about what are roles and systematic methods about how to extract roles still remain unclear.
In this thesis, I advocate for both theories of social science and models of text analysis to better define roles, extract roles, recommend roles to users, and configure roles within the community. Concretely, I focus on four perspectives. The first work constructs profiles of users from what they do and with whom they interact in online cancer support groups, from which we then extract social roles in an unsupervised manner. The second work predicts when and how members transit from one role to another and examine how role contrast helps explain the occurrences of different transitions. Third, I model how the presence of different types of roles and their interaction with task level, group tenure, and group type, predicts group performances. The last perspective investigates whether making the role expectation explicit increases group performance. This work will produce both theoretical and computational results.
For a copy of the thesis proposal, please use the following link: