Reputation and incentives in online social systems
- Online social systems are quickly becoming fundamental to a large part of the modern world, as the platforms for an ever-increasing diversity of aspects of daily life. This thesis aims to develop principled foundations for the reputation and incentive mechanisms that underpin online social systems. These social mechanisms are studied at various levels of resolution: at a microscale, where the atomic units of online social behavior exist; at a mesoscale, where these atomic units coalesce into collective phenomena that affect groups of people; and at a macroscale, where patterns of social interaction in entire communities are guided by the social mechanisms we design. Work in this thesis begins with an examination of the user-to-user evaluations that form the basis of reputation systems across different domains, and shows how relative similarity and relative status play critical roles in shaping these evaluations. We apply this understanding to successfully predict how communities synthesize the many evaluations of a single person into a collective opinion of their reputation. We also investigate how the structure of feedback from social mechanisms can be used to identify content of long-term value in the important domain of question-answering sites. Finally, we introduce a framework for understanding the incentive structures introduced by badge systems. We develop a model for reasoning about user behavior in the presence of badges, and then validate its predictions on real-world data. We find that badges can influence and steer user behavior on a site---leading both to increased participation and to changes in the mix of activities a user pursues. Several robust design principles emerge from our framework that could potentially aid in the design of incentives for a broad range of sites. Finally, we discuss our implementation of a large-scale badge system to 100,000 students on an online education platform. We find a fivefold increase in forum engagement as a result of our system. Overall, we find that studying social mechanisms at all levels of resolution leads to principled foundations that can improve the design of reputation and incentive systems.
|Type of resource
|electronic; electronic resource; remote
|1 online resource.
|Stanford University, Department of Computer Science.
|Jurafsky, Dan, 1962-
|Jurafsky, Dan, 1962-
|Statement of responsibility
|Submitted to the Department of Computer Science.
|Thesis (Ph.D.)--Stanford University, 2016.
- © 2016 by Ashton Anderson
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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