Issue |
E3S Web Conf.
Volume 426, 2023
The 5th International Conference of Biospheric Harmony Advanced Research (ICOBAR 2023)
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Article Number | 02151 | |
Number of page(s) | 7 | |
Section | Innovative Management and Sustainable Society | |
DOI | https://doi.org/10.1051/e3sconf/202342602151 | |
Published online | 15 September 2023 |
Language Features of Transgenders as Their Gender Representation in Digital Culture
English Department, Faculty of Humanities, Bina Nusantara University, Jakarta, Indonesia 11480
* Corresponding author: clara2666@binus.ac.id
This study aims to see what kind of language features used by the transgenders and how the transgenders represented in the digital media which in this case, YouTube. The study will be conducted using the qualitative data analysis. Starting with collecting the data from four videos taken from the YouTube channel of Jimmy Kimmel, Allure, About Ethan, and Netflix. These data then analyzed using the theory of women’s language features by Robin Lakoff, men’s language features by Jennifer Coates, and the theory of gender presentation in digital media by LaFrance and Vial. The result of this study shows that the transgender woman uses six out of ten female language feature such as: lexical hedge/filler, empty adjective, precise color terms, intensifiers, hypercorrect grammar, and superpolite form, while maintaining two of men language feature: harsh words and theme. Meanwhile, the transman maintains three women’s language feature: precise color term, intensifier, and hypercorrect grammar, while using four men language feature such as: backchannel, command and directives, harsh words, and direct questions. The outcome of this study implies that gender need to be considered as a multidimensional construct and more studies about language and gender must be expanded in order for multidimensional identity to be recognized in this vast digital world.
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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