Abstract
Recent studies have shown that counting citations from books can help scholarly impact assessment and that Google Books (GB) is a useful source of such citation counts, despite its lack of a public citation index. Searching GB for citations produces approximate matches, however, and so its raw results need time‐consuming human filtering. In response, this article introduces a method to automatically remove false and irrelevant matches from GB citation searches in addition to introducing refinements to a previous GB manual citation extraction method. The method was evaluated by manual checking of sampled GB results and comparing citations to about 14,500 monographs in the Thomson Reuters Book Citation Index (BKCI) against automatically extracted citations from GB across 24 subject areas. GB citations were 103% to 137% as numerous as BKCI citations in the humanities, except for tourism (72%) and linguistics (91%), 46% to 85% in social sciences, but only 8% to 53% in the sciences. In all cases, however, GB had substantially more citing books than did BKCI, with BKCI's results coming predominantly from journal articles. Moderate correlations between the GB and BKCI citation counts in social sciences and humanities, with most BKCI results coming from journal articles rather than books, suggests that they could measure the different aspects of impact, however.Publisher
WileyJournal
Journal of the Association for Information Science and TechnologyAdditional Links
https://asistdl.onlinelibrary.wiley.com/doi/abs/10.1002/asi.23170Type
Journal articleLanguage
enISSN
2330-1643Sponsors
University of Wolverhamptonae974a485f413a2113503eed53cd6c53
10.1002/asi.23170
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