This paper proposes a methodology which discriminates the articles by the target authors "true" articles from those by other homonymous authors "false" articles . Author name searches for 2,595 "source" authors in six subject fields retrieved about 629 thousands articles. In order to extract true articles from the large amount of the retrieved articles including many false ones, two filtering stages were applied. At the first stage, any retrieved article was eliminated as false if either its affiliation addresses had little similarity to those of its source article or there was no citation relationship between the journal of the retrieved article and that of its source article. At the second stage, a sample of retrieved articles was subjected to manual judgment, and utilizing the judgment results, discrimination functions based on logistic regression were defined. These discrimination functions demonstrated both the recall ratio and the precision of about 95% and the accuracy (correct answer ratio) of 90-95%. Existence of common coauthor(s), address similarity, title words similarity and interjournal citation relationship between the retrieved and source articles were found to be the effective discrimination predictors. Whether or not the source author was from some specific countries was also one of the important predictors. Furthermore, it was shown that a retrieved article is almost certainly true if it was cited by, or cocited with, its source article. The method proposed in this study would be effective when dealing with a large number of articles whose subject fields and affiliation addresses vary widely.
Onodera et al. proposed a methodology for targeting the author from false homonym authors REF .
A method for eliminating articles by homonymous authors from the large number of articles retrieved by author search
J. Assoc. Inf. Sci. Technol.
J. Assoc. Inf. Sci. Technol.
Computer Science