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arXiv:2007.00177cs.CY

De-anonymization of authors through arXiv submissions during double-blind review

Homanga Bharadhwaj, Dylan Turpin, Animesh Garg, Ashton Anderson

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Abstract

In this paper, we investigate the effects of releasing arXiv preprints of papers that are undergoing a double-blind review process. In particular, we ask the following research question: What is the relation between de-anonymization of authors through arXiv preprints and acceptance of a research paper at a (nominally) double-blind venue? Under two conditions: papers that are released on arXiv before the review phase and papers that are not, we examine the correlation between the reputation of their authors with the review scores and acceptance decisions. By analyzing a dataset of ICLR 2020 and ICLR 2019 submissions (n=5050), we find statistically significant evidence of positive correlation between percentage acceptance and papers with high reputation released on arXiv. In order to understand this observed association better, we perform additional analyses based on self-specified confidence scores of reviewers and observe that less confident reviewers are more likely to assign high review scores to papers with well known authors and low review scores to papers with less known authors, where reputation is quantified in terms of number of Google Scholar citations. We emphasize upfront that our results are purely correlational and we neither can nor intend to make any causal claims. A blog post accompanying the paper and our scraping code will be linked in the project website https://sites.google.com/view/deanon-arxiv/home

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Subjects
cs.CYComputers and Society cs.IRInformation Retrieval cs.LGMachine Learning

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    • There's some similar work out that analyses the impact on conference paper acceptance of having deanonymised arXiv versions of papers available before review. They look at ICLR papers for the last 2 years. I've not read it in a lot of detail but it looks like there's a positive…

      aglionbyView on HNParent context

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