Noting the infrastructural turn in platform studies, the article conceives China's health code system, Jian Kang Ma (JKM), deployed to manage the COVID-19 crisis as a new social infrastructure that manifests the symbolic and material power of the Party State. Using the platform walkthrough method and documentary inquiry, we unpack the structures of platform governance and identify actors of the power to appreciate the socio-political dynamics of platform algorithms. JKM's structural power is not monolithic in the name of the Party State but supports a process of structuration that operates across multiple actors, administrative bodies and, governing layers. JKM has centralised data systems through the building of a nationwide algorithmic standard of COVID-19 governance. JKM typified the political dynamics of deterritorialisation, a reference to the state's governing mindset of eradicating local variants of policy implementation and governing autonomy in China. The removal of local power in pandemic administration has led to the production of a unified national subject. Such a comprehensive approach begs for greater nuance and sophisticated knowledge about those indigenous logics that platforms and algorithms operate and are embedded in, thus contributing to de-westernising platform studies.

The advent of the Internet inadvertently augmented the functioning and success of violent extremist organizations. Terrorist organizations like the Islamic State in Iraq and Syria (ISIS) use the Internet to project their message to a global audience. The majority of research and practice on web-based terrorist propaganda uses human coders to classify content, raising serious concerns such as burnout, mental stress, and reliability of the coded data. More recently, technology platforms and researchers have started to examine the online content using automated classification procedures. However, there are questions about the robustness of automated procedures, given insufficient research comparing and contextualizing the difference between human and machine coding. This article compares output of three text analytics packages with that of human coders on a sample of one hundred nonindexed web pages associated with ISIS. We find that prevalent topics (e.g., holy war) are accurately detected by the three packages whereas nuanced concepts (Lone Wolf attacks) are generally missed. Our findings suggest that naïve approaches of standard applications do not approximate human understanding, and therefore consumption, of radicalizing content. Before radicalizing content can be automatically detected, we need a closer approximation to human understanding.

Interaction through online social networks potentially results in the contestation of prevailing ideas about health and health care, and to mass protest where health is put at risk or health care provision is wanting. Through a review of the academic literature and case studies of four social networking health sites (PatientsLikeMe, Mumsnet, Treatment Action Campaign, and My Pro Ana), we establish the extent to which this phenomenon is documented, seek evidence of the prevalence and character of health-related networks, and explore their structure, function, participants, and impact, seeking to understand how they came into being and how they sustain themselves. Results indicate mass protest is not arising from these established health-related networking platforms. There is evidence of changes in policy following campaigning activity prompted by experiences shared through social networking such as improved National Health Service care for miscarriage (a Mumsnet campaign). Platform owners and managers have considerable power to shape these campaigns. Social networking is also influencing health policy indirectly through increasing awareness and so demand for health care. Transient social networking about health on platforms such as Twitter were not included as case studies but may be where the most radical or destabilizing influence on health care policy might arise.