Air pollution, especially at the ground level, poses a high risk for human health as it can have serious negative effects on the population of certain areas. The high variability of this type of data, which are affected by weather conditions and human activities, makes it difficult for conventional methods to precisely detect anomalous values or outliers. In this paper, classical analysis, statistical process control, and functional data analysis are compared for this purpose. The results obtained motivate the development of a new outlier detector based on the concept of functional directional outlyingness. The validation of this algorithm is perfomed on real air quality data from the city of Gijón, Spain, aiming to detect the proven reduction in NO2 levels during the COVID-19 lockdown in that city. Three more variables (SO2, PM10, and O3) are studied with this technique. The results demonstrate that functional data analysis outperforms the two other methods, and the proposed outlier detector is well suited for the accurate detection of outliers in data with high variability.
Resumen El artículo tiene como objetivo conocer el rol que cumplen los principales grupos políticos en España, en la promoción de contenidos desinformativos en Twitter. El estudio aplica análisis estadísticos y de tópico a la total de tuits publicados en español (n = 40.445 tuits), entre septiembre (2019) y febrero (2020), por las cuentas oficiales de los partidos, líderes y portavoces de cada uno de los principales grupos políticos en España (PSOE, Partido Popular, Unidas Podemos, Vox y Ciudadanos); y los contenidos desinformativos identificados, entre agosto 2019 y marzo de 2020, por dos de los principales proyectos periodísticos de Fact-Checking (n = 2.730 contenidos desinformativos) en España (Maldita.es y Newtral.es). Los datos permiten ver, cómo los grupos políticos analizados presentan un nivel alto de coocurrencia con los contenidos desinformativos identificados por Maldita.es y Newtral.es. Lo que estaría confirmando el papel activo de estos actores en la expansión de este tipo de contenidos en Twitter.
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