초록

이 연구에서는 계량정보학적 기법을 사용하여 국내 트위터 관련 연구의 동향을 분석하고자 하였다. 이를 위해 KCI에서 검색된 2009년부터 2014년 4월까지의 트위터 관련 논문 539편에서 제목, 초록, 키워드를 추출하여 분석 자료로 삼았다. 프로파일링 기법을 이용해 트위터 관련 연구가 수행된 학문 분야와 저널을 분석하였고, 동시출현단어 분석을 통해 트위터 관련 연구의 세부 주제 영역을 파악하였다. 그 결과, 국내 트위터 관련 연구는 53개 학문분야에서 다양하게 다루어지고 있으며 핵심 분야는 신문방송학, 경영학, 컴퓨터학 분야로 나타났다. 세부 주제로는 선거를 비롯한 정치 관련 이슈가 가장 많이 다루어졌으며, 기업/구매 관련 이슈도 활발히 연구되었음을 확인할 수 있었다.

Abstract

This study explored the research trends on Twitter in Korea by informetric methods. All 539 articles on Twitter published from 2009 to the April of 2014 were obtained from the KCI. Only article titles, abstracts, and keywords by authors were used in analysis. Academic journals in many different disciplines where Twitter articles were produced were analysed by profiling, and then, the subject areas of researches on Twitter were analysed by co-word analysis. The results of this study showed that Twitter-related papers were published in as many as 53 disciplines with journalism, business administration, and computer science to be core fields. It was also found that the core subject areas are political issues and business.

초록

Abstract

As co-authorship has been prevalent within science communities, counting the credit of co-authors appropriately is an important consideration, particularly in the context of identifying the knowledge structure of fields with author-based analysis. The purpose of this study is to compare the characteristics of co-author credit counting methods by utilizing correlations, multidimensional scaling, and pathfinder networks. To achieve this purpose, this study analyzed a dataset of 2,014 journal articles and 3,892 cited authors from the Journal of the Architectural Institute of Korea: Planning & Design from 2003 to 2008 in the field of Architecture in Korea. In this study, six different methods of crediting co-authors are selected for comparative analyses. These methods are first-author counting (m1), straight full counting (m2), and fractional counting (m3), proportional counting with a total score of 1 (m4), proportional counting with a total score between 1 and 2 (m5), and first-author-weighted fractional counting (m6). As shown in the data analysis, m1 and m2 are found as extreme opposites, since m1 counts only first authors and m2 assigns all co-authors equally with a credit score of 1. With correlation and multidimensional scaling analyses, among five counting methods (from m2 to m6), a group of counting methods including m3, m4, and m5 are found to be relatively similar. When the knowledge structure is visualized with pathfinder network, the knowledge structure networks from different counting methods are differently presented due to the connections of individual links. In addition, the internal validity shows that first-author-weighted fractional counting (m6) might be considered a better method to author clustering. Findings demonstrate that different co-author counting methods influence the network results of knowledge structure and a better counting method is revealed for author clustering.