Silent voices, visible patterns: leveraging partial social interaction graph for vaccination stance detection

Vaccine hesitancy remains a major challenge for pandemic response, and understanding public stances toward vaccination is an important prerequisite for designing effective countermeasures. Existing social-media approaches mainly infer stance from users’ posts, which limits their applicability when many users do not publish vaccination-related textual content and when directly observable stance labels of some users are underused. In this work, we formulate vaccination stance detection as node classification on a partial directed social interaction graph, where edges encode observable interactions and only a subset of users have known stances. We propose GNN-SD, a graph neural network that separately models incoming and outgoing interactions and explicitly incorporates partial known user stances during inference. To benchmark this task, we construct C-VS, the first large-scale labeled social interaction graph for vaccination stance analysis on Twitter. Results show that, using only social interactions and a limited fraction of known user stances, GNN-SD consistently outperforms representative baselines and can identify a substantial proportion of anti-vaccination users without using any users’ vaccination-related textual content. Our research underscores a practical way to identify people with vaccine hesitancy, facilitating public health stakeholders to make more targeted interventions for enhancing vaccine uptake.