<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Korean movies have consistently produced exceptional works that have triggered discussions and spread on social networks in recent years. In this paper, we take the movie language dissemination characteristics in social networks as an anchor point and combine the scale and structure of social networks to improve on the IC model to obtain a movie information dissemination model. This network model and the information cascade tandem are selected as the core of density peak clustering, and improvements are made for the updating rules as well as the propagation order of the multi-strategy labeling algorithm in the DCN algorithm. The characterization of movie language propagation in social networks is completed by using an improved algorithm with a multi-labeling idea. The overall stylistic evolution of Korean movies is examined, and then different language feature styles are compared according to social propagation. In the movie Memories of Murder, at 32 minutes and 23 seconds, which shows a rainy night on a muddy land and conveys the hardship of the protagonist’s situation through visual language, the propagation heat reaches 10.52. Similar to this sentence, the movie depicts a rainy night at 63 minutes and a crime scene at 108 minutes, with the propagation heat of the visual language reaching 19.48 and 23.48, respectively. Based on the propagation analysis of the social networks, the accurate capture of the unique impression of the movie language among the audience.</p>
</abstract>

Korean movies have consistently produced exceptional works that have triggered discussions and spread on social networks in recent years. In this paper, we take the movie language dissemination characteristics in social networks as an anchor point and combine the scale and structure of social networks to improve on the IC model to obtain a movie information dissemination model. This network model and the information cascade tandem are selected as the core of density peak clustering, and improvements are made for the updating rules as well as the propagation order of the multi-strategy labeling algorithm in the DCN algorithm. The characterization of movie language propagation in social networks is completed by using an improved algorithm with a multi-labeling idea. The overall stylistic evolution of Korean movies is examined, and then different language feature styles are compared according to social propagation. In the movie Memories of Murder, at 32 minutes and 23 seconds, which shows a rainy night on a muddy land and conveys the hardship of the protagonist’s situation through visual language, the propagation heat reaches 10.52. Similar to this sentence, the movie depicts a rainy night at 63 minutes and a crime scene at 108 minutes, with the propagation heat of the visual language reaching 19.48 and 23.48, respectively. Based on the propagation analysis of the social networks, the accurate capture of the unique impression of the movie language among the audience.

Linguistic Characteristics and Stylisation of Korean Cinema Based on the Social Network Analysis Method

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Korean movies have consistently produced exceptional works that have triggered discussions and spread on social networks in recent years. In this paper, we...

Linguistic Characteristics and Stylisation of Korean Cinema Based on the Social Network Analysis Method

Online veröffentlicht: 25. Nov. 2023

Seitenbereich: -

Eingereicht: 07. März 2023

Akzeptiert: 05. Juni 2023

DOI: https://doi.org/10.2478/amns.2023.2.01222

Schlüsselwörter
Social network analysis, IC model, DCN algorithm, Multi-hashtag strategy, Korean movies, Linguistic feature style

© 2023 Sai Xu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Linguistic Characteristics and Stylisation of Korean Cinema Based on the Social Network Analysis Method

Online veröffentlicht: 25. Nov. 2023

Seitenbereich: -

Eingereicht: 07. März 2023

Akzeptiert: 05. Juni 2023

DOI: https://doi.org/10.2478/amns.2023.2.01222

SchlüsselwörterSocial network analysis, IC model, DCN algorithm, Multi-hashtag strategy, Korean movies, Linguistic feature style

© 2023 Sai Xu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Social network analysis, IC model, DCN algorithm, Multi-hashtag strategy, Korean movies, Linguistic feature style