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Exploiting Classical Bibliometrics of CSCW: Classification, Evaluation, Limitations, and the Odds of Semantic Analytics

  • António Correia
  • Benjamim Fonseca
  • Hugo Paredes
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7946)

Abstract

The purpose of this study is to conduct a bibliometric and taxonomy-based analysis of the field of CSCW to map its recent evolution at a quantitative and qualitative level. A model for semantic analytics and social evaluation is also discussed with emphasis on the hypothesis of putting crowds into the loop of bibliography classification process to improve the current labor-intensive, time-consuming, unrepeatable and sometimes subjective task of scientometricians. A total of 1,480 publications were carefully reviewed and subjected to scientometric data analysis methods and techniques. Analyzed parameters included document orientation, deviation from trend in the total number of citations, and publication activity by author’s affiliation country. A semantic classification of 541 publications allows identifying growing trends and lacking research indicators. At a human-centered perspective, limitations are unfilled in the limitative analytical spectrum, laborious and time-consuming processes of data seeking, gathering, cataloguing and analysis, subjective results at a taxonomic level, lack of more bibliographic data analytics perspectives, and absence of human-centered results concerning cognitive aspects in meta-knowledge research practices. Hypotheses are suggested towards a crowd-enabled model for bibliography evaluation in order to understand the ways as humans and machines can work cooperatively and massively on scientific data to solve complex problems.

Keywords

Bibliographic data analytics crowd-enabled model CSCW HCI human computation scientometrics semantics taxonomy 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • António Correia
    • 1
  • Benjamim Fonseca
    • 2
  • Hugo Paredes
    • 2
  1. 1.UTAD – University of Trás-os-Montes e Alto DouroVila RealPortugal
  2. 2.INESC TEC/UTADVila RealPortugal

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