Extant research demonstrated that the algorithms of the Kremlin-controlled search engine Yandex, compared to those of its US-based counterpart Google, frequently produce results that are biased toward the interests of Russia’s ruling elites. Prior research, however, audited Yandex’s algorithms largely within Russia. In contrast, this study is the first to assess the role of Yandex’s web search algorithms as a resource for Russia’s informational influence abroad. To do so, we conduct a comparative algorithm audit of Google and Yandex in Belarus, examining the visibility and narratives of COVID-19-related conspiracy theories in their search results. By manually analysing the content of 1,320 search results collected in mid-April to mid-May 2020, we find that, compared with Google, (1) Yandex retrieves significantly more conspiratorial content (2) that close to exclusively suspects US plotters to be behind the pandemic, even though the virus spread from the Chinese city of Wuhan across the globe.

This article considers the politics and practicalities of responding to the COVID crisis with ‘an app for that’. It shows how seductive solutionism in times of crisis created political impetus to direct the public health response to contact tracing through Contact Tracing Apps (CTA). Rather than focus on user-based concerns (uptake, privacy, etc.), we’ve investigated how apps interface with complex systems and infrastructures of public health. Our 21 expert informants from five developed nations offered insight into the machinations of contact tracing from ‘the coal face’ up to executive technical and policy decisions including national CTA development and deployment. We learned that beneath the shiny veneer of an app is the messy certitude of Excel and tech-debt, politics, and mundane organizational technique that worked amidst each other to shape public health. Our approach provides a more nuanced understanding of the interfaces of CTA and digital epidemiology than current App narratives allow.

While a healthy and critical literature on digital app interventions into COVID-19 has developed, there has not been critical consideration of these apps informed by insights from those responsible for designing, implementing, and making use of these digital tools. We redress this research imbalance by considering how user-centric narratives of the platformization of public health can gloss over what situational analysis (Clarke et al., 2016) might better uncover. This paints a more nuanced picture of digital epidemiology than current App narratives provide to address the contingent promises and failures related to these digital technologies.

During COVID-19, countless dashboards have served as central media where people learn critical information about the pandemic. Varied actors, including news organizations, government agencies, universities, and NGOs created and maintained these dashboards, conducting the onerous labor of collecting, categorizing, and taking care of COVID data. This study uncovers different forms of data practices and labor behind the building of these dashboards, based on in-depth interviews with volunteers and practitioners across India and the United States who have participated in COVID dashboard projects.

Specifically, we are interested in projects that have focused on underrepresented or missing COVID data such as COVID cases in prisons and long-term care facilities, racial/ethnic breakdown of cases, as well as deaths due to COVID enforcement. These data builders employed sometimes creative, sometimes mundane and laborious data practices to not simply collect, but to produce these data that are often invisible in the official COVID dataset. In this process of data production, dashboard builders grappled with the questions of how certain data is collected, who/what is missing from the dataset, and how these data voids shape and manipulate our understanding of the pandemic. Interviewing 74 data builders who participated in COVID dashboard projects, this paper demonstrates the range of underrepresented and messy COVID data that these data builders have identified, fixed, and maintained to render them useful: disappearing data, lumped data, and absent data. Such critical engagement with messy COVID data reveals different data injustices that have tremendous potential to affect future pandemic preparation and management.