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A machine-learning approach to coding book reviews as quality indicators: Toward a theory of megacitation

Journal of the Association for Information Science and Technology, 2014, Vol.65 (11), p.2248-2260 [Peer Reviewed Journal]

2014 ASIS&T ;2015 INIST-CNRS ;ISSN: 2330-1635 ;EISSN: 2330-1643 ;DOI: 10.1002/asi.23104

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  • Title:
    A machine-learning approach to coding book reviews as quality indicators: Toward a theory of megacitation
  • Author: Zuccala, Alesia ; van Someren, Maarten ; van Bellen, Maurits
  • Subjects: Assessments ; Bibliometrics ; Bibliometrics. Scientometrics ; Bibliometrics. Scientometrics. Evaluation ; Classification ; Classifiers ; Coding ; Evaluation ; Exact sciences and technology ; Indicators ; Information and communication sciences ; Information science. Documentation ; Library and information science. General aspects ; Machine learning ; Manuals ; Quality ; Sciences and techniques of general use ; Sentences
  • Is Part Of: Journal of the Association for Information Science and Technology, 2014, Vol.65 (11), p.2248-2260
  • Description: A theory of “megacitation” is introduced and used in an experiment to demonstrate how a qualitative scholarly book review can be converted into a weighted bibliometric indicator. We employ a manual human‐coding approach to classify book reviews in the field of history based on reviewers' assessments of a book author's scholarly credibility (SC) and writing style (WS). In total, 100 book reviews were selected from the American Historical Review and coded for their positive/negative valence on these two dimensions. Most were coded as positive (68% for SC and 47% for WS), and there was also a small positive correlation between SC and WS (r = 0.2). We then constructed a classifier, combining both manual design and machine learning, to categorize sentiment‐based sentences in history book reviews. The machine classifier produced a matched accuracy (matched to the human coding) of approximately 75% for SC and 64% for WS. WS was found to be more difficult to classify by machine than SC because of the reviewers' use of more subtle language. With further training data, a machine‐learning approach could be useful for automatically classifying a large number of history book reviews at once. Weighted megacitations can be especially valuable if they are used in conjunction with regular book/journal citations, and “libcitations” (i.e., library holding counts) for a comprehensive assessment of a book/monograph's scholarly impact.
  • Publisher: Malden, MA: Blackwell Publishing Ltd
  • Language: English
  • Identifier: ISSN: 2330-1635
    EISSN: 2330-1643
    DOI: 10.1002/asi.23104
  • Source: Alma/SFX Local Collection
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