Data and Code for "Hydrology Meets Public Attention and Policy Change: Integrating a Multiple Streams Framework and Human Social Sensing into Sociohydrological Analysis"

This dataset contains the data and Python scripts used for the computational text analysis reported in the manuscript “Hydrology Meets Public Attention and Policy Change: Integrating a Multiple Streams Framework and Human Social Sensing into Sociohydrological Analysis.” The materials support reproduction of the Human Social Sensing (HSS) workflow based on environmental news articles, including data collection, Korean-language text preprocessing, topic-number selection using coherence and perplexity, and the final four-topic Latent Dirichlet Allocation (LDA) analysis. The resulting topic proportions were aggregated annually to construct topic-specific indicators of media attention used in the sociohydrological analysis. The archive includes the input data, analysis scripts, and a README file describing the repository structure and recommended execution order.