Representing Research Attention as Contextually Structured Flows
arXiv:2606.05895
The paper introduces attention flows, a way to represent research attention with contextual structure instead of simple counts, and shows that dynamic contextualized representations can capture relationships between research outputs, evaluated via an analogy benchmark.
Abstract
Research metrics use attention as evidence of societal impact. Yet attention serves as evidence only once interpreted, and its meaning depends on its contextual structure, not on volume alone. Altmetrics represents signals in isolation, keeping a count of the attention an output received, or a sequence of when. We address this with attention flows, representations that situate an output's attention in the contexts through which it is distributed. To evaluate the flow, we build a benchmark of analogy queries, each testing whether the relationship between two outputs, applied to a third, yields a fourth. The count and sequence baselines fail to recover these relationships, whereas flows learned as dynamic contextualised representations recover them. The recovered structure also survives partial observation and rests on its contexts instead of volume. These findings support attention represented as contextually structured for research evaluation.
Accepted at STi 2026 - International Conference on Science and Technology Indicators