A Study on the Interactive Narrative - Focusing on the analysis of VR animation <Wolves in the Walls> (인터랙티브 내러티브에 관한 연구 - VR 애니메이션 <Wolves in the Walls>의 분석을 중심으로)
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- Trans-
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- v.15
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- pp.25-56
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- 2023
VR is a dynamic image simulation technology with very high information density. Among them, spatial depth, temporality, and realism bring an unprecedented sense of immersion to the experience. However, due to its high information density, the information contained in it is very easy to be manipulated, creating an illusion of objectivity. Users need guidance to help them interpret the high density of dynamic image information. Just like setting up navigation interfaces and interactivity in games, interactivity in virtual reality is a way to interpret virtual content. At present, domestic research on VR content is mainly focused on technology exploration and visual aesthetic experience. However, there is still a lack of research on interactive storytelling design, which is an important part of VR content creation. In order to explore a better interactive storytelling model in virtual reality content, this paper analyzes the interactive storytelling features of the VR animated version of <Wolves in the walls> through the methods of literature review and case study. We find that the following rules can be followed when creating VR content: 1. the VR environment should fully utilize the advantages of free movement for users, and users should not be viewed as mere observers. The user's sense of presence should be fully considered when designing interaction modules. Break down the "fourth wall" to encourage audience interaction in the virtual reality environment, and make the hot media of VR "cool". 2.Provide developer-driven narrative in the early stages of the work so that users are not confused about the ambiguous world situation when they first enter a virtual environment with a high degree of freedom. 1.Unlike some games that guide users through text, you can guide them through a more natural interactive approach that adds natural dialog between the user and story characters (NPC). Also, since gaze guidance is an important part of story progression, you should set up spatial scene user gaze guidance elements within it. For example, you can provide eye-following cues, motion cues, language cues, and more. By analyzing the interactive storytelling features and innovations of the VR animation <Wolves in the walls>, I hope to summarize the main elements of interactive storytelling from its content. Based on this, I hope to explore how to better showcase interactive storytelling in virtual reality content and provide thoughts on future VR content creation.
From January 2020 to October 2021, more than 500,000 academic studies related to COVID-19 (Coronavirus-2, a fatal respiratory syndrome) have been published. The rapid increase in the number of papers related to COVID-19 is putting time and technical constraints on healthcare professionals and policy makers to quickly find important research. Therefore, in this study, we propose a method of extracting useful information from text data of extensive literature using LDA and Word2vec algorithm. Papers related to keywords to be searched were extracted from papers related to COVID-19, and detailed topics were identified. The data used the CORD-19 data set on Kaggle, a free academic resource prepared by major research groups and the White House to respond to the COVID-19 pandemic, updated weekly. The research methods are divided into two main categories. First, 41,062 articles were collected through data filtering and pre-processing of the abstracts of 47,110 academic papers including full text. For this purpose, the number of publications related to COVID-19 by year was analyzed through exploratory data analysis using a Python program, and the top 10 journals under active research were identified. LDA and Word2vec algorithm were used to derive research topics related to COVID-19, and after analyzing related words, similarity was measured. Second, papers containing 'vaccine' and 'treatment' were extracted from among the topics derived from all papers, and a total of 4,555 papers related to 'vaccine' and 5,971 papers related to 'treatment' were extracted. did For each collected paper, detailed topics were analyzed using LDA and Word2vec algorithms, and a clustering method through PCA dimension reduction was applied to visualize groups of papers with similar themes using the t-SNE algorithm. A noteworthy point from the results of this study is that the topics that were not derived from the topics derived for all papers being researched in relation to COVID-19 (