Sociohydrological models seek to represent co-evolving human and water systems. Yet, static assumptions or poorly constrained parameters may limit their capacity to reproduce long-term transitions in societal discourse and policy priorities that shape hydrologic outcomes. To address this gap, we propose and evaluate a human social sensing (HSS) framework that converts long-term public discourse into quantitative, time-resolved proxy indicators associated with water-related discourse dynamics in sociohydrological systems. HSS integrates large-scale text mining with structured manual coding to capture scalable patterns and interpretable context in water-related narratives. We applied this framework to 61 years (1960–2020) of South Korean newspaper coverage, revealing a clear transition from disaster- and development-centered framing to sustained attention on water quality, environmental management, and ecosystem protection, alongside a tonal reorientation from economic development priorities toward environmental sustainability. Text mining identified structural changes in themes and salient terms, including the rising prominence of water-quality and water-resource themes. Meanwhile, manual coding provided an interpretable contextual benchmark for evaluating computational outputs and for identifying periods in which discourse shifts preceded institutional change. Together, the proposed framework generates high-resolution indicators of evolving water-related societal discourse and demonstrates the complementary strengths of computational analysis and human interpretation. By generating empirically derived discourse-level proxy indicators, HSS offers a structured framework for incorporating long-term societal framing and public discourse dynamics into sociohydrological analysis, while preserving interpretive caution about the indirect relationship between media discourse and public perception.