Social media platforms have an increasingly central influence on global politics. Media of unprecedented reach, they have the power to sway elections, exacerbate societal polarization, promote or provoke conflict at all levels, and jeopardize relations between states. But what of the people who govern and oversee these platforms? For although algorithms and automation may underpin how social media content influences politics, the policies, approaches, and international relations of social media companies are directed or conducted by corporate executives and their representatives, actors who receive limited critical attention in International Relations (IR) scholarship. Combining multiple data sources, including field interviews with Meta and Twitter staff on three continents, this reflection suggests an approach to studying social media companies and their relationships to global politics that moves beyond abstraction and aggregation. Examining these actors and their internal dynamics through an organizational lens can shed fresh light on the contingent spatial, temporal, and normative drivers and enactments of their influence across the international system.

Significant heterogeneity in network structures reflecting individuals’ dynamic processes can exist within subgroups of people (e.g., diagnostic category, gender). This makes it difficult to make inferences regarding these predefined subgroups. For this reason, researchers sometimes wish to identify subsets of individuals who have similarities in their dynamic processes regardless of any predefined category. This requires unsupervised classification of individuals based on similarities in their dynamic processes, or equivalently, in this case, similarities in their network structures of edges. The present paper tests a recently developed algorithm, S-GIMME, that takes into account heterogeneity across individuals with the aim of providing subgroup membership and precise information about the specific network structures that differentiate subgroups. The algorithm has previously provided robust and accurate classification when evaluated with large-scale simulation studies but has not yet been validated on empirical data. Here, we investigate S-GIMME’s ability to differentiate, in a purely data-driven manner, between brain states explicitly induced through different tasks in a new fMRI dataset. The results provide new evidence that the algorithm was able to resolve, in an unsupervised data-driven manner, the differences between different active brain states in empirical fMRI data to segregate individuals and arrive at subgroup-specific network structures of edges. The ability to arrive at subgroups that correspond to empirically designed fMRI task conditions, with no biasing or priors, suggests this data-driven approach can be a powerful addition to existing methods for unsupervised classification of individuals based on their dynamic processes.