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Driving the Closest Information. Local Data
Journalism in the UK
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Título : Driving the Closest Information. Local Data
Journalism in the UK |
Autor : Arias Robles, Félix López, Pilar José |
Editor : Taylor and Francis Group |
Departamento: Departamentos de la UMH::Ciencias Sociales y Humanas |
Fecha de publicación: 2020-04 |
URI : https://hdl.handle.net/11000/30885 |
Resumen :
This article analyses the rise of local data journalism in the United
Kingdom from a triple perspective: the description of four cases,
their visions on the situation of this specialty and their
perspectives on the theoretical keys of this phenomenon. Semistructured
interviews with the editors of these four projects are
the main research method. The results show a complementary
media ecosystem, due to their different editorial models and
approaches. Journalists mainly compose the project teams,
although they collaborate with technical profiles either within the
company or through external collaborations. Data analysis is the
fundamental task and, therefore, certain knowledge of statistics
and spreadsheets, in addition to the conventional journalistic
values, remain the main requirements. Local media are going
through a critical situation, with little opportunities to invest in
innovation, but precisely data journalism can become a solution if
it contributes to the stability of the media outlets such as the rest
of the newsroom. Therefore, this research aims to show the
relevance of data journalism in the present and in the future of
local and regional media and, consequently, in the improvement
of the closest and most influential information.
|
Palabras clave/Materias: Data journalism local journalism investigative journalism journalistic profession news production media ecosystem artificial intelligence |
Área de conocimiento : CDU: Generalidades.: Ciencia y conocimiento. Investigación. Cultura. Humanidades. |
Tipo de documento : info:eu-repo/semantics/article |
Derechos de acceso: info:eu-repo/semantics/closedAccess Attribution-NonCommercial-NoDerivatives 4.0 Internacional |
DOI : https://doi.org/10.1080/17512786.2020.1749109 |
Aparece en las colecciones: Artículos Ciencias Sociales y Humanas
|
La licencia se describe como: Atribución-NonComercial-NoDerivada 4.0 Internacional.