Author(s)

Weiwei Zhang ¹

Affiliation(s)

¹ School of International Studies, Sun Yat-sen University, Zhuhai, China

Corresponding Author

Weiwei Zhang

ABSTRACT

This study explores the ideological and political elements in Guangdong's localized Korean language textbooks using Social Network Analysis. It analyzes Korean perceptions of Guangdong through social media text mining technology and discusses integrating Guangdong culture into Korean textbooks to enhance students' ability to tell Chinese stories in Korean. This study reveals that the most frequent keywords included "person", "China", "Hong Kong" and "food" with a notable association between Guangdong and Hong Kong. Topic modeling identified four main themes which are "Guangdong Cuisine", "WayV", "Return to Blossoming Blade" and "Guangdong and Other Regions". These findings suggest integrating Guangdong's culinary culture, popular culture, and literary works into Korean language textbooks to enhance communication and students' Korean ability, while also foster a sense of national pride and belonging.

KEYWORDS

Social Network Analysis, Korean language textbooks, Guangdong, Ideological and political education, Netminer, Text mining

CITE THIS PAPER

Weiwei Zhang, Exploration of the Ideological and Political Elements in Korean Language Textbooks in Guangdong through Social Network Analysis. Journal of Sociology and Ethnology (2024) Vol. 6: 147-152. DOI: http://dx.doi.org/10.23977/jsoce.2024.060521.

REFERENCES

[1] Ding Yi(2022), A study on Curriculum Reform of Korean Education in Chinese Universities from the Perspective of Curriculum-Based Political and Virtuous Awareness, Studies of Chinese & Korean Humanities, 75, pp. 165-193.
[2] Jiang Baoyou (2022), The Status Quo and Issues of Korean Language Education in China, Dongjiang Journal, 39(01), pp. 88-93+128. 
[3] Wen Qiufang (2021), The Connotation and Implementation Framework of Ideological and Political Education in College Foreign Language Courses, Foreign Languages in China, 18(02), pp. 47-52.
[4] Gerlach, M., Peixoto, T. P., & Altmann, E. G.(2018), A network approach to topic models, Science Advances, 4(7), pp. 1-11.
[5] Lim, Y. S., Park, H. W.(2011), Profile analysis of top Twitter users, Journal of the Korean Data Analysis Society, 13(1), pp. 45-52.
[6] Smith, M. A., Rainie, L., Shneiderman, B., Himelboim, I.(2014), Mapping Twitter Topic Networks: From Polarized Crowds to Community Clusters, Washington: Pew Research Internet Project.

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