Talk by Dr. Minsu Park (New York University Abu Dhabi)

Title: The Impact of Social Traces on User Perceptions & Behaviors on Online Pages.

Abstract: Recent studies have documented racial discrimination in online interactions, mirroring the historic bias observed offline. The sharing economy is especially vulnerable due to greater dependence on mutual trust in sharing a ride, residence, or date with a stranger. These services rely on user recommendations to build trust, but the effects of these peer evaluations on racial bias are only beginning to be explored. Using data from Airbnb, we examine in-group preference for same-race hosts as well as same-race recommendations. The unexpected result is that these two manifestations of racial bias are offsetting, not reinforcing. Guests largely overcame their racial bias in host selection when hosts were endorsed by previous same-race guests. Moreover, we found no evidence of racial bias in the affective enthusiasm of endorsements, which suggests that the preference for same-race endorsements is motivated by the race of the recommender, not the content of the recommendation.

Bio: Minsu Park is an Assistant Professor of Social Research and Public Policy at New York University Abu Dhabi. He develops and applies quantitative and computational methods to study the consumption and production of creative work. His current projects focus on how cultural artifacts/interests flow worldwide and how social traces, such as ratings, reviews, and reviewer identities, shape audiences’ perceptions and engagements online. His research inhabits an interdisciplinary nexus between data science and social science, simultaneously drawing on and contributing to both, and has been published in top-tier venues in both computer and information science conferences (e.g., ICWSM) and interdisciplinary journals (e.g., Science Advances, Nature Human Behaviour). He received his doctorate in Information Science at Cornell University, where he was a member of the Social Dynamics Lab. He is also affiliated with the Center for Data Science at New York University.