Visualizing Research Trends: A Big Data Perspective on Servicewomen Studies

Abstract

Utilizing big data visualization, this study decodes the dynamic scholarly landscape of U.S. servicewomen research (1981-2024). Through analyses on publication time, keywords co-occurrence, citation bursts and their clustering relationships of 1,268 publications (Web of Science Core Collection) via CiteSpace 6.1.R6, we revealed: (1) Policy-pulsed knowledge production; (2) Triple thematic shifts - from battlefield health risks to mental health crises post-combat integration, formulating reproductive health policy responses; (3) Algorithmic clustering paradox - ten thematic clusters expose structural imbalance; (4) Big data diagnosed institutional gender blindness. We argue that big data visualization effectively captures epistemic power dynamics but risks reifying patriarchal frameworks without critical interrogation.