The Oxford Internet Institute is excited to welcome Prof Young Mie Kim from the University of Wisconsin-Madison for the talk "Russia, Cambridge Analytica, and What Else? Groups and Targets behind Divisive Issue Campaigns on Facebook".

In light of Russian interference in the 2016 U.S elections and the Cambridge Analytica scandal, the present research asks the question of whether the digital media has become the stealth media for anonymous political campaigns. By utilizing user-based, real-time, digital ad tracking tool, the present research reverse engineers and tracks the groups (Study 1) and the targets (Study 2) of divisive issue campaigns based on 5 million paid ad impressions on Facebook exposed to 9,519 individuals between September 28 to November 8, 2016. The findings revealed that anonymous groups–unidentifiable “suspicious” groups, astroturf/movement/unregistered groups, and nonprofits who did not file a report to Federal Election Commission—ran most of the divisive issue campaigns. One out of six suspicious groups later turned out to be Kremlin-linked groups. Anonymous groups clearly targeted battleground states including Pennsylvania, Wisconsin, where Democratic strongholds turned to support Trump by razor-thin margins. The present research offers insight relevant for regulatory policies and discusses the normative implications for the functioning of democracy.

About the speakers

  • Professor Young Mie Kim

    Affiliation: University of Wisconsin-Madison

    Young Mie Kim is a Professor of the School of Journalism and Mass Communication and Faculty Affiliate of the Department of Political Science at the University of Wisconsin-Madison. Her research focuses on the politics in the age of data-driven digital media, especially political communication among political leaders, non-party groups, and citizens. Her recent research, Project Data (Data Ad Tracking & Analysis), uncovers the behind scenes of political campaigns on digital platforms–unknown actors, furtive messaging, and imperceptive microtargeting.

This page was last modified on 29 May 2018