The anticipation of food crises is a critical component of humanitarian aid efforts aimed at reducing human suffering. However, existing predictive models often rely on risk measures that are inadequate due to delays, outdated information, or incomplete data. A new study utilizing recent advances in deep learning and analyzing over 11.2 million news articles from 1980 to 2020 on food-insecure countries has identified high-frequency precursors to food crises that are both interpretable and validated by traditional risk indicators. This groundbreaking research provides a significant advancement in predicting food insecurity by leveraging the power of machine learning and big data analytics. To read more, use the button below to open the original external article.