Same message, different interpretation from K-pop fandom
On September 14, 2018, RM, the leader of BTS, gave a powerful speech to the UN General Assembly, which became a significant moment in K-pop history. His message encouraged people to "speak [them]selves (…) no matter who [they] are, where [they’re] from, [their] skin color, gender identity." His mention of gender identity was significant to international ARMYs and positioned him as an advocate for LGBTQ+ human rights. While the speech also resonated in Korea, it did so for different reasons. Despite K-pop being produced in a conservative and heteronormative society, it challenges gender norms worldwide. This article will explore the following questions: (1) How did RM's speech resonate differently among international and Korean K-pop fandoms? (2) What does this discrepancy reveal about BTS' influence on their fans? The study uses on-site fan interviews and social media analysis of tweets to answer these questions.
In this paper, I provide a comparative analysis of K-pop fandom's reactions to BTS's speech at the United Nations. To do so, I used a theoretical framework that mixed the background of Korean society and performative conceptions of gender and adapted them to the nature of the audiences I was looking at. As a result, I used the concept of “localized perception of gender” to complicate the difference in the sensibility of Korean and non-Korean fans to RM’s speech. Finally, to advocate for an interdisciplinary approach to Korean Studies, I used data science techniques and ethnographical fieldwork.
The results of the study show that the non-Korean fans of BTS use the UN speech as a way to express their views regarding gender identity. They were particularly receptive to the part of the speech that dealt with that issue, allowing them to re-interpret other BTS-related content, such as the lyrics of their songs or their TV appearances. This queering process of BTS by the non-Korean fandom of K-pop is quite different as it focuses on real events rather than fan-produced content such as fan fiction or fan videos.
The Korean fans of BTS also used BTS’ UN speech to express themselves but with an additional factor of their place in society. With a focus on the self, the Korean fans used this powerful moment to overcome the difficulties of their daily lives that mainly deal with trying to fit into Korean society. Their reactions also added a nationalist perspective to the speech, showing Korean ARMYS proud of their idols representing Korea in front of the rest of the world. Even if it might sound like non-Korean fandom is particularly focused on gender issues and that the Korean fandom was not as receptive to that part, it is essential to note that there are exceptions, such as the making of the Rainbow ARMY Scout.
In both fandoms, the common thread of self-expression or self-love appears as a continuity of BTS's message through their Love Yourself Trilogy. Here, we made a simple distinction between “Korean” and “Non-Korean” fandoms, but to make this study even more insightful, it is important to dig deeper into cultural values such as the self in different cultural backgrounds. Also, because this study mainly focuses on BTS, it would be interesting to look at other K-pop idols that have a message of self-love, compare it with BTS, and see if there was a similar process of queer interpretation by the fans.
The following Notebook excerpts provide greater details concerning the result of the text analysis of the corpora used in the project. It shows the code that leads to the results exploited in the paper and wider results and methods to explore them further.
Methodology
To explore the opinions of international and Korean fans, I will combine two different study methods. The first one will be Internet ethnography, a qualitative study method that consists of participant observation of the Internet by analyzing content such as Internet community postings or social media conversations. This largely qualitative approach will be augmented by data analysis of Twitter feeds. Twitter will be the most useful social media since it is a platform for interaction between international and Korean K-pop fans. Moreover, it is not only the most-used platform by BTS to communicate with their fans but also the platform chosen by UNICEF to promote the “Love Myself” campaign.
Since it is impossible to retrieve tweets older than seven days through Twitter’s API, I had to use, to avoid this limitation, a tool called GetOldTweets3 working on Python3. To scrap the reactions of the non-Korean fans of BTS, I used the keywords “BTS+UN+Speech.” I added that the tweets should be in English and gathered 14,258 tweets. For the Korean fans, I used the queries “방탄+유엔+연설” (lit. Bangtan+uen+yŏnsŏl) and “방탄+UN+연설” (lit. Bangtan+UN+yŏnsŏl) and collected a total of 2,806 tweets. Through both processes, I have been able to compile tweets uploaded during a period going from September 2018 to November 2019. To provide a better understanding of the methodology and more detailed results, this paper goes along with a companion Jupyter notebook that the reader can access through a repository uploaded on my GitHub account.
Preparation of the tweet corpora and tokenization
The tweets were compiled in two separate CSV files, one for the international and the other for the Korean fandom. The first task was to extract the content of the tweets in each CSV file to create our Twitter corpora (cf. Fig 1a. & 1b. of the companion notebook). To do so, I read the CSV files using Pandas, one of the most common data analysis libraries, working on Python. I put the content of each column cell into a single string (cf. Fig 1c. of the companion notebook).
Once the corpora were compiled, the next step was to tokenize and clean the data. According to Manning, Raghavan, and Schütze, “given a character sequence and a defined document unit, tokenization is the task of chopping it up into pieces, called tokens.” I tokenized the international fans' reactions corpus using the Natural Language Toolkit (NLTK), which “provides basic classes for representing data relevant to natural language processing [in Python.]"
For the corpus showing the Korean fans' reactions, it was impossible to use NLTK. One of the main challenges in this study was using text analysis tools with corpora in two different languages. Korean is an agglutinative language; space was not a sufficient unit of tokenization. As a result, I used a specific set of tools for natural Korean language processing called KoNLPy. KoNLPy gathers various tokenization classes made with different dictionaries. I chose Open Korean Text (Okt), “an open-source Korean tokenizer written in Scala, developed by Will Hohyon Ryu.” The main reason for choosing Okt over the other classes was that it was created by a developer who worked at Twitter and, as a result, had a dictionary built on Twitter corpora. For both corpora, it was also important to filter the content of the tweets using a list of stop words. In Natural Language Processing, stop words are “common words that appear of little value in helping select documents matching a user need.” For the English corpus, I used NTLK’s pre-set stop words list. I also extended it with terms that distorted the data, such as the keywords used during the search query, BTS members, hyperlinks, or unrelated hashtags. For the Korean corpus, I used a pre-made list of stop words that I extended with the same equivalent of the unnecessary keywords in Korean. As I was processing the text analysis of the corpora, I came across new stop words to add, and I had to edit them numerous times. For the code that leads to the “clean” corpora, refer to Fig. 1d. and 1e. of the companion notebook.