A multi-platform framework for nowcasting social phenomena: A case study for food insecurity

Given the growing significance of internet-based information flows, this research proposes a conceptual framework that integrates digital platforms to nowcast social phenomena, applied to the context of food security monitoring in the Global South. Building on the foundations of Digital Methods and online issue mapping, our research objective is to establish a multi-modal, multi-media model that monitors events from different perspectives to identify potential early warning signals arising from the data, ultimately informing policy actors and supporting early action. We apply three analytical processes: social listening, media monitoring and search interest analysis. Exploratory analysis on data from Zimbabwe point to the feasibility of the models applied to identify food security dimensions in text and search engine data. Further analysis is needed to interpret converging and diverging trends across the data streams, and their implications to food insecurity early warning.