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Título : | Unboxing Computational Social Media Research From a Datahermeneutical Perspective: How Do Scholars Address the Tension Between Automation and Interpretation? |
Otros títulos : | International Journal of Communication |
Autor : | Jünger, Jacob Geise, Stephanie Hänelt, Maria |
Palabras clave : | science social methods |
Fecha de publicación : | 2022 |
Editorial : | International Journal of Communication |
Citación : | Jünger, J., Geise, S., and Hänelt, M. (2022). Unboxing Computational Social Media Research From a Datahermeneutical Perspective: How Do Scholars Address the Tension Between Automation and Interpretation?. International Journal Of Communication, 16, 24. https://ijoc.org/index.php/ijoc/article/view/17477/3714 |
Resumen : | Communication researchers have fruitfully applied computational methods in their analysis of communication processes. However, the automation of scientific data collection and analysis confronts scholars with fundamental epistemological and practical challenges. Particularly, automation implies that the processing of data is highly standardized for all cases. In the context of social science research, this contrasts with the expectation that meaning is always attributed in individual interaction processes. Based on a literature review of peer-reviewed journal articles, our study explores the resulting tension between automated and interpretive research. We first analyze the extent to which automated methods play a role in social media research. We then identify the challenges and limitations researchers addressed in their studies. On this basis, we propose steps for a data hermeneutical perspective that combines computational methods with interpretive approaches. |
URI : | https://repositorio.consejodecomunicacion.gob.ec//handle/CONSEJO_REP/8567 |
ISSN : | 1932-8036 |
Aparece en las colecciones: | Documentos internacionales sobre libertad de expresión y derechos conexos |
Ficheros en este ítem:
Fichero | Descripción | Tamaño | Formato | |
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Unboxing computational.pdf | Unboxing computational | 667,83 kB | Adobe PDF | Visualizar/Abrir |
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