The Myth of the Double-Blind Review? Author Identification Using Only Citations
|Keywords:||KDD Cup Competition;author identification;social network analysis;relational learning;vector-space model;discriminative self-citations|
|Citation:||Volume 5, Issue 2 - Page 179|
|Abstract:||Prior studies have questioned the degree of anonymity of the double-blind review process for scholarly research articles. For example, one study based on a survey of reviewers concluded that authors often could be identified by reviewers using a combination of the author's reference list and the referee's personal background knowledge. For the KDD Cup 2003 competition's "Open Task" we examined how well various automatic matching techniques could identify authors within the competition's very large archive of research papers. This paper describes the issues surrounding author identification, how these issues motivated our study, and the results we obtained. The best method, based on discriminative self-citations, identified authors correctly 40-45% of the time. One main motivation for double-blind review is to eliminate bias in favor of well-known authors. However, identification accuracy for authors with substantial publication history is even better (60% accuracy for the top-10% most prolific authors, 85% for authors with 100 or more prior papers).|
|Appears in Collections:||CeDER Published Papers|
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