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Anticipating the outbreak of a food crisis is crucial to efficiently allocateemergency relief and reduce human suffering. However, existing food insecurityearly warning systems rely on risk measures that are often delayed, outdated,or incomplete. Here, we leverage recent advances in deep learning to extracthigh-frequency precursors to food crises from the text of a large corpus ofnews articles about fragile states published between 1980 and 2020. Our textfeatures are causally grounded, interpretable, validated by existing data, andallow us to predict 32% more food crises than existing models up to threemonths ahead of time at the district level across 15 fragile states. Theseresults could have profound implications on how humanitarian aid gets allocatedand open new avenues for machine learning to improve decision making indata-scarce environments.

Fine-grained prediction of food insecurity using news streams