• Title/Summary/Keyword: Analysis of Wikipedia Usage Data

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An Ontology-based Analysis of Wikipedia Usage Data for Measuring degree-of-interest in Country (국가별 관심도 측정을 위한 온톨로지 기반 위키피디아 사용 데이터 분석)

  • Kim, Hyon Hee;Jo, Jinnam;Kim, Donggeon
    • Journal of the Korea Society of Computer and Information
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    • v.19 no.4
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    • pp.43-53
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    • 2014
  • In this paper, we propose an ontology-based approach to measuring degree-of-interest in country by analyzing wikipedia usage data. First, we developed the degree-of-interest ontology called DOI ontology by extracting concept hierarchies from wikipedia categories. Second, we map the title of frequently edited articles into DOI ontology, and we measure degree-of-interest based on DOI ontology by analyzing wikipedia page views. Finally, we perform chi-square test of independence to figure out if interesting fields are independent or not by country. This approach shows interesting fields are closely related to each country, and provides degree of interests by country timely and flexibly as compared with conventional questionnaire survey analysis.

A Study of Collective Knowledge Production Mechanisms of the three Great SNS (3대 SNS에서의 집단적 지식생산 메커니즘 연구)

  • Hong, Sam-Yull;Oh, Jae-Chul
    • The Journal of the Korea institute of electronic communication sciences
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    • v.8 no.7
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    • pp.1075-1081
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    • 2013
  • Twitter, Facebook, and KakaoStory are the major SNS in Korea. Social knowledge production is being produced by those services from numerous collaboration and co-participation in those SNS. Wikipedia or Naver JishikIN service was regarded as the representative product of collective knowledge production during the wired internet era. However now at the wireless internet era centered with smart phones, various forms of collective knowledge production would be achieved by connecting to SNS in real-time. In this thesis, the survey data of collective knowledge production for users of three SNS have been compared and analyzed. The difference of the collective knowledge production mechanism among Twitter, Facebook and KakaoStory has been studied and compared through three variables: the motivation of collective knowledge production, the preference of collective knowledge production model, and collective knowledge production cultural perception. As a result of the analysis of the discriminant factors for three SNS user groups, it turns out that the diversity-toward usage motivation, personal contribution motivation, and collective knowledge production tendency perception are the most influential variables. This thesis is of significance in that it unites the value of social science such as social capital and collective knowledge production from the viewpoint of computer science and opens the new chapter of collective knowledge production with the real-time SNS of wireless internet from the wired internet.