Cómo estudiar la construcción de la imagen de una ciudad a través de publicaciones de Instagramuna metodología aplicada a Granada
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Universidad de Granada
info
ISSN: 2659-9538
Año de publicación: 2019
Título del ejemplar: Metodologies for Communication Research
Volumen: 1
Número: 2
Páginas: 7-20
Tipo: Artículo
Otras publicaciones en: Comunicación & métodos
Resumen
Our digital world is increasingly visual. Mobile applications focused on digital photography such as Instagram are vehicles for the creation, manipulation and instant dissemination of images. Therefore, Instagram is an open window to researching in Social Sciences and Digital Humanities, and an opportunity to investigate how the young users of this application are developing visual culture in their local environments through global visual languages. This work reviews the methodology used to study it through the analysis of Instagram production in Granada from a sample of 955,564 publications and 375,758 posts and geolocated images collected over a year (between April 2017 and 2018), with the aim of showing how the image of a city is socially constructed.
Referencias bibliográficas
- Boy, J. D., & Uitermark, J. (2016). How to Study the City on Instagram. PLoS ONE, 11(6). doi: 10.1371/journal.pone.0158161
- Confessore, N. (2018, enero 27). The Follower Factory. The New York Times. Recuperado de https://www.nytimes.com/interactive/2018/01/27/technology/social-media-bots.html
- Dhar, V. (2013). Data science and prediction. Communications of the ACM, 56(12), 64-73. doi: 10.1145/2500499
- Facebook. (2018). Instagram Graph API Launches and Instagram API Platform Deprecation. Recuperado de https://developers.facebook.com/blog/post/2018/01/30/instagram-graph-api-updates
- Gephi Consortium. (2018). Gephi. Recuperado de https://gephi.orgGoogle. (2018). Análisis de contenido de imágenes con API Vision | Cloud Vision API. Recuperado de https://cloud.google.com/vision/?hl=es
- Gruzd, A. (2016). Netlytic: Software for automated text and social network analysis. Communication & Methods, Vol. 1, nº2, pp. 7-2018 Recuperado de https://netlytic.org
- Hand, M. (2014a). Digitization and memory: Researching practices of adaption to visualand textual data in everyday life. En Big Data? Qualitative Approaches to DigitalResearch (pp. 205–227). Recuperado de http://www.emeraldinsight.com/doi/pdf/10.1108/S1042-319220140000013013
- Hand, M. (2014b). From cyberspace to the dataverse: Trajectories in digital social research. En Big Data? Qualitative Approaches to Digital Research (pp. 1–27). Recuperado de http://www.emeraldinsight.com/doi/full/10.1108/S1042-319220140000013002
- Hand, M. (2017). Visuality in Social Media: Researching images, circulations and practices. En The SAGE Handbook of Social Media Research Methods. SAGE.
- Hayashi, C. (1998). What is Data Science ? Fundamental Concepts and a Heuristic Example. En C. Hayashi, K. Yajima, H.-H. Bock, N. Ohsumi, Y. Tanaka, & Y. Baba (Eds.), Data Science, Classification, and Related Methods (pp. 40-51). doi: 10.1007/978-4-431-65950-1_3
- Instagram. (2018). Instagram Developer Documentation. Recuperado de https://www.instagram.com/developer
- Manjoo, F. (2018, febrero 9). Welcome to the Post-Text Future. The New York Times. Recuperado de https://www.nytimes.com/interactive/2018/02/09/technology/the-rise-of-a-visual-internet.html
- Manovich, L. (2009). Cultural analytics: Visualising cultural patterns in the era of “more media”. Domus March.Manovich, L. (2012). How to compare one million images? En Understanding digital humanities (pp. 249–278). Recuperado de https://link.springer.com/chapter/10.1057/9780230371934_14
- Manovich, L. (2017). Cultural analytics, social computing and digital humanities. The datafied society: studying culture through data, 55. Recuperado de http://www.oapen.org/download?type=document&docid=624771#page=56
- Manovich, L., Goddemeyer, D., Stefaner, M., & Baur, D. (2015). On Broadway. Recuperado de http://on-broadway.nyc/
- Manovich, L., & Indaco, A. (2017). The Image of a Data City: Studying the Hyperlocal with Social Media. Architectural Design, 87(1), 110-117. doi: 10.1002/ad.2140
- Manovich, L., Stefaner, M., Yazdani, M., Baur, D., Goddemeyer, D., & Tifentale, A. (2014). Selfiecity. Recuperado de http://selfiecity.net
- Martín Prada, J. (2018). El ver y las imágenes en el tiempo de Internet.Mirzoeff, N. (2016). Cómo ver el mundo: Una nueva introducción a la cultura visual. Grupo Planeta Spain.
- QGIS Development Team. (2018). QGIS Geographic Information System. Open SourceGeospatial Foundation Project. Recuperado de https://qgis.orgR Development Core Team. (2008). R: A Language and Environment for Statistical Computing. Recuperado de http://www.r-project.org
- Rose, G. (2016). Visual methodologies: An introduction to researching with visual materials. Sage.
- Rueden, C. T., Schindelin, J., Hiner, M. C., DeZonia, B. E., Walter, A. E., Arena, E. T., & Eliceiri, K. W. (2017). ImageJ2: ImageJ for the next generation of scientific image data. BMC Bioinformatics, 18(1). doi: 10.1186/s12859-017-1934-z
- Schneider, C. A., Rasband, W. S., & Eliceiri, K. W. (2012). NIH Image to ImageJ: 25 years of image analysis. Nature Methods, 9(7), 671-675.
- Smith, A. R. (1978). Color gamut transform pairs. ACM Siggraph Computer Graphics, 12(3), 12–19.
- Van Rossum, G. (2007). Python Programming Language. USENIX Annual Technical Conference, 41, 36