Abstract: Reputation systems are crucial to online platforms’ health. They are prevalent across online marketplaces and social media platforms either visibly (e.g., as star ratings and badges) or invisibly as signals that feed into recommendation and moderation engines. In theory, good behavior (e.g., honest, accurate, high-quality) begets high reputation, while poor behavior is deterred and pushed off the platform.
In this talk, I will discuss how these systems seem to fulfill this mission only coarsely: reputation alone is not a strong predictor of success nor disappearance. On one platform, we were able to predict 2 times more suspensions than the reputation system in place using other public signals. In another platform, we found that users with high reputation signals were suspended at significantly lower rates (up to 3 times less) for the same number of offenses and behavior as regular users, which may be counterproductive to platform health. I will provide some hypotheses to explain these results and offer preliminary findings towards improving these signals.
Bio: Alejandro is a 5th year PhD student at Carnegie Mellon University in Societal Computing, advised by Prof. Nicolas Christin. He is interested in measuring social influence in online communities adjacent to underground economies. His recent work focuses on how reputation is leveraged in anonymous marketplaces, p2p marketplaces, and cryptocurrency communities. He is a recipient of a CMU Cylab Presidential Fellowship, as well as a IEEE S&P Distinguished Paper Award. Prior to CMU, he obtained a B.S. from The Pennsylvania State University, where he worked with Prof. Peng Liu and Prof. Xinyu Xing on a variety of systems security projects. A Paraguayan native, Alejandro has been invited to talk about his work at the Paraguayan Central Bank and the Paraguayan National Police.