Nowcasting food insecurity interest with google trends data

dc.creatorCaravaggio, Nicola
dc.creatorCarneiro, Bia
dc.creatorResce, Giuliano
dc.date2024-07-15
dc.date2024-07-25T20:21:52Z
dc.date2024-07-25T20:21:52Z
dc.date.accessioned2026-06-27T13:34:57Z
dc.descriptionThis research explores the potential of Google Trends (GT) data as a tool for generating a daily index of food insecurity at the national level, focusing on regions monitored by the Famine Early Warning Systems Network (FEWS NET) and the Global Fragility Act (GFA). Drawing inspiration from previous studies on GT's predictive capabilities, the authors employ Natural Language Processing (NLP) to analyse food security reporting from FEWS NET documents. We identify key predictors of food insecurity using a LASSO regression approach and construct a daily economic sentiment index (DESI) for each country. Unlike traditional methods, the study considers multiple languages and weights search terms based on LASSO coefficients. The resulting Synthetic Search Interest (SSI) index for food insecurity demonstrates a statistically significant correlation with FAO's share of the population in severe food insecurity, affirming GT's potential as a monitoring tool. The research contributes a novel methodology and insights into leveraging real-time data for early warnings in food security.
dc.formatapplication/pdf
dc.identifierhttps://hdl.handle.net/10568/149276
dc.identifier.urihttp://hdl.handle.net/123456789/63927
dc.languageen
dc.publisherUniversitat Politècnica de València
dc.rightsOpen Access
dc.sourceCaravaggio, N.; Carneiro, B.; Resce, G. (2024) Nowcasting food insecurity interest with google trends data. 6th International Conference on Advanced Research Methods and Analytics (CARMA 2024). Valencia, 26-28 June 2024. 7 p.
dc.subjectmachine learning
dc.subjectfood security
dc.subjectearly warning systems
dc.subjectnatural language processing
dc.subjectnowcasting
dc.subjectgoogle trends
dc.titleNowcasting food insecurity interest with google trends data
dc.typeConference Paper

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