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Are all literature citations equally important? Automatic citation strength estimation and its applications
Author(s) -
Wan Xiaojun,
Liu Fang
Publication year - 2014
Publication title -
journal of the association for information science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.903
H-Index - 145
eISSN - 2330-1643
pISSN - 2330-1635
DOI - 10.1002/asi.23083
Subject(s) - citation , bibliometrics , computer science , scientometrics , impact factor , value (mathematics) , information retrieval , statistics , set (abstract data type) , data science , field (mathematics) , regression analysis , citation analysis , data mining , mathematics , library science , machine learning , political science , law , pure mathematics , programming language
Literature citation analysis plays a very important role in bibliometrics and scientometrics, such as the S cience C itation I ndex ( SCI ) impact factor, h‐index. Existing citation analysis methods assume that all citations in a paper are equally important, and they simply count the number of citations. Here we argue that the citations in a paper are not equally important and some citations are more important than the others. We use a strength value to assess the importance of each citation and propose to use the regression method with a few useful features for automatically estimating the strength value of each citation. Evaluation results on a manually labeled data set in the computer science field show that the estimated values can achieve good correlation with human‐labeled values. We further apply the estimated citation strength values for evaluating paper influence and author influence, and the preliminary evaluation results demonstrate the usefulness of the citation strength values.