TY - JOUR
KW - Bibliometrics
KW - Citation analysis
KW - Decision making
KW - Machine Learning
KW - Observational studies
KW - Parametric analysis
KW - peer review
KW - Test statistics
AU - Ivan Stelmakh
AU - Charvi Rastogi
AU - Ryan Liu
AU - Shuchi Chawla
AU - Federico Echenique
AU - Nihar B. Shah
AB - Citations play an important role in researchers’ careers as a key factor in evaluation of scientific impact. Many anecdotes advice authors to exploit this fact and cite prospective reviewers to try obtaining a more positive evaluation for their submission. In this work, we investigate if such a citation bias actually exists: Does the citation of a reviewer’s own work in a submission cause them to be positively biased towards the submission? In conjunction with the review process of two flagship conferences in machine learning and algorithmic economics, we execute an observational study to test for citation bias in peer review. In our analysis, we carefully account for various confounding factors such as paper quality and reviewer expertise, and apply different modeling techniques to alleviate concerns regarding the model mismatch. Overall, our analysis involves 1,314 papers and 1,717 reviewers and detects citation bias in both venues we consider. In terms of the effect size, by citing a reviewer’s work, a submission has a non-trivial chance of getting a higher score from the reviewer: an expected increase in the score is approximately 0.23 on a 5-point Likert item. For reference, a one-point increase of a score by a single reviewer improves the position of a submission by 11% on average.
BT - PLOS ONE
DA - Jul 7, 2023
DO - 10.1371/journal.pone.0283980
IS - 7
LA - en
N2 - Citations play an important role in researchers’ careers as a key factor in evaluation of scientific impact. Many anecdotes advice authors to exploit this fact and cite prospective reviewers to try obtaining a more positive evaluation for their submission. In this work, we investigate if such a citation bias actually exists: Does the citation of a reviewer’s own work in a submission cause them to be positively biased towards the submission? In conjunction with the review process of two flagship conferences in machine learning and algorithmic economics, we execute an observational study to test for citation bias in peer review. In our analysis, we carefully account for various confounding factors such as paper quality and reviewer expertise, and apply different modeling techniques to alleviate concerns regarding the model mismatch. Overall, our analysis involves 1,314 papers and 1,717 reviewers and detects citation bias in both venues we consider. In terms of the effect size, by citing a reviewer’s work, a submission has a non-trivial chance of getting a higher score from the reviewer: an expected increase in the score is approximately 0.23 on a 5-point Likert item. For reference, a one-point increase of a score by a single reviewer improves the position of a submission by 11% on average.
PY - 0
EP - e0283980
ST - Cite-seeing and reviewing
T2 - PLOS ONE
TI - Cite-seeing and reviewing: A study on citation bias in peer review
UR - https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0283980
VL - 18
Y2 - 2024-09-30
SN - 1932-6203
ER -