Predicting food crises using news streams

Anticipating food crisis outbreaks is crucial to efficiently allocate emergency relief and reduce human suffering. However, existing predictive models rely on risk measures that are often delayed, outdated, or incomplete. Using the text of 11.2 million news articles focused on food-insecure countries and published between 1980 and 2020, we leverage recent advances in deep learning to extract high-frequency precursors to food crises that are both interpretable and validated by traditional risk indicators. We demonstrate that over the period from July 2009 to July 2020 and across 21 food-insecure countries, news indicators substantially improve the district-level predictions of food insecurity up to 12 months ahead relative to baseline models that do not include text information. These results could have profound implications on how humanitarian aid gets allocated and open previously unexplored avenues for machine learning to improve decision-making in data-scarce environments.

Automated summary

Anticipating food crisis outbreaks is crucial to efficiently allocate emergency relief and reduce human suffering. However, existing predictive models often rely on delayed, outdated, or incomplete risk measures. In this study, we utilize deep learning techniques to analyze 11.2 million news articles focused on food-insecure countries and extract high-frequency precursors to food crises. The results show that incorporating news indicators significantly improves district-level predictions of food insecurity up to 12 months in advance, providing new insights for decision-making in data-scarce environments.