Disinformation and misinformation have become targets of public and political discourse as major threats to democracy in the digital age. What strategies should be employed to combat these problems? In this talk I will focus on rumors as a class of misinformation that is inherently difficult to identify. Classically defined as propositions about fundamentally ambiguous situations, rumors are difficult exactly because nobody knows whether they are true or false. Further complicating matters, rumors can serve the public good when they spread valuable true information. To address these challenges, I will present a novel, empirically grounded, computational perspective on rumoring. I will use this lens to advocate for systems that promote rational uncertainty represented at the population level as an approach to designing for rumors. Drawing on the theory of collective intelligence, I will then argue that promoting information sharing and healthy discussion is a more promising route to this end than censorship mechanisms.
I am a Moore/Sloan & WRF Innovation in Data Science Postdoctoral Fellow at the University of Washington, where I am mentored by Emma Spiro in the Information School’s DataLab. I study rumors and collective intelligence, often from the perspective of cognitive science. In addition to my position at UW, I am also a part-time postdoctoral researcher with Tom Griffiths at the University of California Berkeley’s Social Science Matrix, and I recently spent a little time as a visiting postdoctoral scholar at the Data & Society Research Institute in New York City. Previous to being an itinerant postdoc, I was a PhD student at MIT co-advised by Sandy Pentland and Josh Tenenbaum in EECS, CSAIL, the Media Lab, and the Department of Brain and Cognitive Sciences. I was also once a Master’s student at UMass Amherst with Hanna Wallach, and an undergraduate researcher at UMass with Andy Barto. My graduate research was support by an NSF Graduate Research Fellowship.