A multimodal approach for regional GDP prediction using social media activity and historical information
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http://hdl.handle.net/10045/116638
Título: | A multimodal approach for regional GDP prediction using social media activity and historical information |
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Autor/es: | Ortega Bastida, Javier | Gallego, Antonio-Javier | Rico-Juan, Juan Ramón | Albarrán, Pedro |
Grupo/s de investigación o GITE: | Reconocimiento de Formas e Inteligencia Artificial | Economía Laboral y Econometría (ELYE) |
Centro, Departamento o Servicio: | Universidad de Alicante. Departamento de Lenguajes y Sistemas Informáticos | Universidad de Alicante. Departamento de Fundamentos del Análisis Económico |
Palabras clave: | Machine learning | Neural networks | Gross Domestic Product (GDP) | Knowledge based economy |
Área/s de conocimiento: | Lenguajes y Sistemas Informáticos | Fundamentos del Análisis Económico |
Fecha de publicación: | 13-jul-2021 |
Editor: | Elsevier |
Cita bibliográfica: | Applied Soft Computing. 2021, 111: 107693. https://doi.org/10.1016/j.asoc.2021.107693 |
Resumen: | This work proposes a multimodal approach with which to predict the regional Gross Domestic Product (GDP) by combining historical GDP values with the embodied information in Twitter messages concerning the current economic condition. This proposal is of great interest, since it delivers forecasts at higher frequencies than both the official statistics (published only annually at the regional level in Spain) and the existing unofficial quarterly predictions (which rely on economic indicators that are available only after months of delay). The proposed method is based on a two-stage architecture. In the first stage, a multi-task autoencoder is initially used to obtain a GDP-related representation of tweets, which are then filtered to remove outliers and to obtain the GDP prediction from the consensus of opinions. In a second stage, this result is combined with the historical GDP values of the region using a multimodal network. The method is evaluated in four different regions of Spain using the tweets written by the most relevant economists, politicians, newspapers and institutions in each one. The results show that our approach successfully learns the evolution of the GDP using only historical information and tweets, thus making it possible to provide earlier forecasts about the regional GDP. This method also makes it possible to establish which the most or least influential opinions regarding this prediction are. As an additional exercise, we have assessed how well our method predicted the effect of the COVID-19 pandemic. |
Patrocinador/es: | This work was supported by the Pattern Recognition and Artificial Intelligence Group (PRAIg) at the University of Alicante, Spain |
URI: | http://hdl.handle.net/10045/116638 |
ISSN: | 1568-4946 (Print) | 1872-9681 (Online) |
DOI: | 10.1016/j.asoc.2021.107693 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/article |
Derechos: | © 2021 Elsevier B.V. |
Revisión científica: | si |
Versión del editor: | https://doi.org/10.1016/j.asoc.2021.107693 |
Aparece en las colecciones: | INV - ELYE - Artículos de Revistas INV - GRFIA - Artículos de Revistas |
Archivos en este ítem:
Archivo | Descripción | Tamaño | Formato | |
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Ortega-Bastida_etal_2021_ApplSoftComput_accepted.pdf | Accepted Manuscript (acceso abierto) | 984,3 kB | Adobe PDF | Abrir Vista previa |
Ortega-Bastida_etal_2021_ApplSoftComput_final.pdf | Versión final (acceso restringido) | 563,57 kB | Adobe PDF | Abrir Solicitar una copia |
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