Harnessing public sentiment discourse for early drought detection and water crisis response for strategic water management and resilient policy planning

The extensive and gradual onset of drought prompts critical examination of the alterations and engagement among substantial demographics during the drought's advancement and the consequent effects of such shifts on drought detection. This research examines Fb data from 2016 to 2024 to investigate the role of online engagement in drought management. This study evaluates public discourse on drought-related matters through the analysis of five fundamental terms. The research employs topic modeling and sentiment analysis to assess regional awareness and utilizes machine learning techniques (Random Forest, Naive Bayes) in conjunction with Bag of Words to forecast drought progression. The research highlights the potential of Fb data in facilitating real-time drought management, offering significant hydrological insights. The study elucidates regional disparities in drought awareness through the examination of key terminology and sentiment, revealing that some regions exhibit a more rapid reaction to water scarcity, as indicated by Fb engagement. Furthermore, the incorporation of machine learning algorithms such as Random Forest and Naive Bayes facilitates a predictive paradigm for detecting prospective drought hotspots through online discourse analysis. The study confirmed that participation in online communities successfully (p ≤ 0.04) alleviates the impact of drought and, on Facebook, significantly enhanced drought awareness across various regions of Pakistan (p ≤ 0.5), as confirmed through statistical analysis with a paired t-test and regression analysis over labeled sentiment and topic-classified data. Fb engagement may function as a proactive indicator, assisting policymakers and hydrologists in optimizing water resource allocation in drought-prone areas, thus enhancing drought mitigation strategies.