Exploring the reception of foreign language music in the English-speaking world with the use of Digital Humanities techniques

Armstead, Charlie (2023). Exploring the reception of foreign language music in the English-speaking world with the use of Digital Humanities techniques. University of Birmingham. M.A.

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Abstract

This thesis presents an analysis of the reception of popular foreign language music in the English-speaking world with the use of a corpus of YouTube comments. This analysis is carried out with the use of a variety of computational methodologies and other Digital Humanities techniques. We initially situate ourselves within the wider authorship of scholars employing similar corpora to us and also provide a framework for the collection and curation of a corpus of YouTube comments. Then, in the pursuit of our analysis, we narrow our focus to three case studies: an investigation into the role of fandom amongst English-speaking K-Pop fans, an examination of language-attitudes in the comment sections of reggaeton songs, and finally an analysis of the role of the meme in viral songs alongside a novel methodology to this end.

Throughout these case studies, methodologies of varying complexity are employed and encompass fields of computer science ranging from machine learning, computational linguistics, and computational discourse analysis. With this in mind, in order to comply with Digital Humanities ethics and values, a secondary aim of this project is the careful documentation and justification of methodological techniques employed throughout. This is to ensure maximal reproducibility for future research and most of the scripts and datasets produced during this project can be found at https://ursidaeic.github.io/.

In each of the case studies, we succeed in elucidating aspects of the digital culture of YouTube with regards to the respective focus of the study. However, we conclude that, as this is such an understudied area of digital culture, there still remains much scope for future research which will be greatly facilitated with the methodologies and datasets produced throughout this project.

Type of Work:

Thesis (Masters by Research > M.A.)

Award Type:

Masters by Research > M.A.

Supervisor(s):