Evaluating wider impacts of books via fine-grained mining on citation literatures

Qingqing Zhou and Chengzhi Zhang ()
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Qingqing Zhou: Nanjing Normal University
Chengzhi Zhang: Nanjing University of Science and Technology

Scientometrics, 2020, vol. 125, issue 3, No 5, 1923-1948

Abstract: Abstract Citations are commonly used to measure academic impacts of scientific publications, including books. However, citation frequencies of books are single numerical evaluation metrics. It neglects details about books (e.g. contents), which may lead to the decline in comprehensiveness of evaluation results. Hence, fine-grained mining on books’ citation information to integrate frequency metrics and content metrics can obtain more reliable evaluation results. Books’ citation literatures (i.e. literatures cited books) present citation frequencies of books, and reflect citation intentions, topics and domains simultaneously. Existing research focused on analysing citation frequencies, authors or citation contexts of citation literatures to conduct citation analysis. It may be costly for collecting citation contexts and neglected latent information of citation literatures, such as impact scopes or topics of books reflected by citation literatures. Therefore, in this paper, we conducted fine-grained analysis on books’ citation literatures to assess whether citation literatures could be systematically used for indicators of books’ wider impacts. Specifically, we firstly collected books and corresponding information about their citation literatures. Then, we extracted multi-dimensional metrics via multi-granularity mining on citation literatures, and got assessment results by integrating content-level and frequency-level metrics. Finally, we compared assessment results based on citation literatures and existing metrics for assessing books’ impacts to verify assessment results. Experimental results infer that citation literatures are a promising source for book impact assessment, especially books’ academic impacts.

Keywords: Wider impact assessment; Content mining; Topic model; Citation analysis; Library holdings (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s11192-020-03676-2

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