Nowcasting food insecurity interest with google trends data
This 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...
| Main Authors: | , , |
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| Format: | Conference Paper |
| Language: | Inglés |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/149276 |
| _version_ | 1855537650759368704 |
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| author | Caravaggio, Nicola Carneiro, Bia Resce, Giuliano |
| author_browse | Caravaggio, Nicola Carneiro, Bia Resce, Giuliano |
| author_facet | Caravaggio, Nicola Carneiro, Bia Resce, Giuliano |
| author_sort | Caravaggio, Nicola |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | This 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. |
| format | Conference Paper |
| id | CGSpace149276 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| record_format | dspace |
| spelling | CGSpace1492762025-11-05T12:31:10Z Nowcasting food insecurity interest with google trends data Caravaggio, Nicola Carneiro, Bia Resce, Giuliano machine learning food security early warning systems natural language processing nowcasting google trends This 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. 2024-07-15 2024-07-25T20:21:52Z 2024-07-25T20:21:52Z Conference Paper https://hdl.handle.net/10568/149276 en Open Access application/pdf Caravaggio, 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. |
| spellingShingle | machine learning food security early warning systems natural language processing nowcasting google trends Caravaggio, Nicola Carneiro, Bia Resce, Giuliano Nowcasting food insecurity interest with google trends data |
| title | Nowcasting food insecurity interest with google trends data |
| title_full | Nowcasting food insecurity interest with google trends data |
| title_fullStr | Nowcasting food insecurity interest with google trends data |
| title_full_unstemmed | Nowcasting food insecurity interest with google trends data |
| title_short | Nowcasting food insecurity interest with google trends data |
| title_sort | nowcasting food insecurity interest with google trends data |
| topic | machine learning food security early warning systems natural language processing nowcasting google trends |
| url | https://hdl.handle.net/10568/149276 |
| work_keys_str_mv | AT caravaggionicola nowcastingfoodinsecurityinterestwithgoogletrendsdata AT carneirobia nowcastingfoodinsecurityinterestwithgoogletrendsdata AT rescegiuliano nowcastingfoodinsecurityinterestwithgoogletrendsdata |