Dean Eckles

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Dean Eckles

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Dean Eckles is the Mitsubishi Career Development Associate Professor and an Associate Professor of Marketing at MIT Sloan. He is affiliate faculty at the Institute for Data, Systems & Society in the Schwarzman College of Computing.

His substantive research examines people's interactions with and through communication technologies, especially the ways these technologies mediate, amplify, and direct social influence. This work sometimes requires or benefits from new analytical methods, so Eckles also works on applied statistics, design of field experiments, and causal inference.

Prior to joining MIT, he was a scientist at Facebook, where he worked on many product areas and analytical methods, including News Feed, messaging, advertising, tools for randomized experiments, and survey methods. Eckles previously worked in research at Nokia and Yahoo.

Eckles received his BA in philosophy, a BS and MS in cognitive science, an MS in statistics, and a PhD in communication, all from Stanford University.

Honors

Aral and Eckles win for a working paper

Eckles wins Amazon Research Award

Publications

"Perverse Consequences of Debunking in a Twitter Field Experiment: Being Corrected for Posting False News Increases Subsequent Sharing of Low Quality, Partisan, and Toxic Content."

Mohsen Mosleh, Cameron Martel, Dean Eckles, and David G. Rand. In Proceedings of the 2021 ACM CHI Virtual Conference on Human Factors in Computing Systems, New York, NY:. Forthcoming.

"Spillover Effects in Experimental Data."

Aronow, Peter M., Dean Eckles, Cyrus Samii, and Stephanie Zonszein. In Cambridge Handbook of Advances in Experimental Political Science, edited by Donald P. Green and James Druckmann, Cambridge, UK: Cambridge University Press. Forthcoming. arXiv Preprint.

"Targeting for Long Term Outcomes."

Yang, Jeremy, Dean Eckles, Paramveer Dhillon, and Sinan Aral. Management Science. Forthcoming. arXiv Preprint.

"Evaluating Stochastic Seeding Strategies in Networks."

Chin, Alex, Dean Eckles, and Johan Ugander. Management Science Vol. 68, No. 3 (2022): 1714-1736. arXiv Preprint.

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