On May 7, 2020, the Center for a New American Security (CNAS) hosted a virtual event coinciding with the release of a new CNAS report, Dangerous Synergies: Countering Chinese and Russian Digital Influence Operations, by Daniel Kliman, Andrea Kendall-Taylor, Kristine Lee, Joshua Fitt, and Carisa Nietsche.
Senior Fellow and Director of the Asia Program, CNAS
Senior Fellow and Director of the Transatlantic Security Program, CNAS
Research Assistant, CNAS
Research Associate, CNAS
Expert Panel Discussion
Foreign Affairs Reporter, Formiche
China Reporter, Axios
Senior Fellow and Director of the Alliance for Securing Democracy
German Marshall Fund
Associate Fellow, CNAS
The 2016 U.S. presidential election and the 2018 and 2020 Taiwanese local and presidential elections crystallized Russia and China’s use of digital interference to shape the contest between democracies and autocracies. This is occurring in real time as China and Russia seek to obscure the origins of COVID-19, while Beijing cynically recasts itself as the global leader in responding to the very pandemic it failed to contain.
As the authors argue in this major CNAS report, there is growing evidence of strategic convergence in Beijing and Moscow’s digital influence campaigns. Their efforts have already proven mutually reinforcing on several key issues—ranging from 5G deployment to the Hong Kong pro-democracy protests to the COVID-19 pandemic. Beijing and Moscow are working in tandem to undermine liberal democratic norms and institutions; weaken cohesion among democratic allies and partners; reduce U.S. global influence; and, advance their respective positions. In light of the deepening synergies between these two powers, the United States and its democratic allies and partners should adopt a holistic approach, including creative cost imposition measures to counter digital influence campaigns by China and Russia. Absent this, Beijing and Moscow are poised to accelerate their complementary efforts to bend the arc of the global information landscape to their joint advantage.