빅데이터 분석 기반 문화콘텐츠 학위논문 연구 동향 (2004년∼2020년)

Abstract

This study aims to collect data on theses related to cultural contents published from 2004 through 2020, and analyze the main research subjects, topics, and research trends, using big data analysis. Furthermore, this study will establish the academic system in the field of cultural contents research. For data collection, this study gathered and analyzed abstracts and bibliographical information of 1,812 theses from 2004 to 2020, using the keyword “cultural contents” in the department information search engine. This study was conducted broadly in four steps, including “data collection,” “data pre-processing,” “data analysis,” and “synthesis and interpretation.” Big data analyses were conducted using NetMiner. The results of the study can be summarized as follows, focusing on the research questions presented in Chapter 1. First, due to the co-occurrence frequency analysis, “culture” had the highest frequency, followed by “utilization,” “contents,” “Korea,” “society,” “value,” etc. These words were commonly used when the authors prepared theses. Second, this study identified and examined the words that had high co-occurrence frequency, degree centrality, and eigenvector centrality. The words that had high values of the three indices can be the main research subjects in the field of studies of cultural contents or the keywords that influence the preparation of related texts. The examination revealed that “culture,” “society,” “Korea,” “people,” “value,” “utilization,” “contents” etc, were keywords or the main research subjects. Third, keyword network analysis of data was conducted by classifying the timeline into Period 1 (2004-2007), Period 2 (2008-2012), Period 3 (2013-2016), and Period 4 (2017-2020), to examine the change and flow of research in the field of theses on cultural contents, and the result was compared. According to the co-occurrence frequency and centrality analysis, the value of utilization was high in all periods. The analysis showed that utilization was an important research subject in theses on cultural contents throughout the periods. The significance of this paper was that it enabled us to understand research subjects, the result of big data analysis, and the change and flow in the studies of cultural contents by collecting and analyzing academic data accumulated to date in the cultural contents research field. Additionally, the contents of the abstracts were intuitively comprehensible because the research results were presented in the form of knowledge maps.