mlsanityData to 14 Oct 2026

Which Review Aspect Has a Greater Impact on the Duration of Open Peer Review in Multiple Rounds? -- Evidence from Nature Communications

arXiv 2606.21904 ↗First posted 20 Jun 2026cs.CL cs.DL cs.HCcited by 1

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Abstract (arXiv)

Purpose: Peer review is essential to scientific publishing, but increasing submission volumes have placed growing pressure on reviewers and editors. This study examines the relationship between sentiment toward specific review aspects and peer review duration. It also investigates how this relationship varies across disciplines and review rounds, with the aim of supporting targeted manuscript revision and improving review efficiency. Design/methodology/approach: We adopt a two-stage approach. First, fine-grained aspects are extracted from peer review reports, and a sentiment classification model is used to determine the sentiment associated with each aspect. Second, correlations between aspect-level sentiment and peer review duration are analyzed. Sentiment scores are also calculated for different review rounds to determine whether these relationships change over successive rounds. Findings: Review sentiment has a weak but statistically significant negative correlation with peer review duration, indicating that more positive reviews tend to be associated with shorter review periods. Aspects concerning Evaluation and Results and Impact and Research Value show relatively stronger correlations with review duration. The relationships between aspect-level sentiment and review duration also differ significantly across review rounds. Originality/value: This study connects the textual content of peer review reports with the temporal characteristics of the review process. By identifying review aspects that are more closely associated with review duration, it provides evidence that may help authors prioritize revisions and assist reviewers and editors in improving review efficiency. The findings contribute to reducing the burden of peer review and accelerating scholarly communication and knowledge dissemination.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 16 of its 61 references that are in the map. First posted on arXiv 20 Jun 2026.

Builds on

16 of 61 references.

References · 16

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2018A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applicationscited by 136
  3. 2017Interactive Attention Networks for Aspect-Level Sentiment Classificationcited by 91
  4. 2016Aspect Level Sentiment Classification with Deep Memory Networkcited by 82
  5. 2020ReviewRobot: Explainable Paper Review Generation based on Knowledge Synthesiscited by 54
  6. 2019Attentional Encoder Network for Targeted Sentiment Classificationcited by 48
  7. 2019Argument Mining for Understanding Peer Reviewscited by 41
  8. 2018Aspect Level Sentiment Classification with Attention-over-Attention Neural Networkscited by 24
  9. 2020Aspect-based Sentiment Analysis of Scientific Reviewscited by 24
  10. 2020Argument Mining Driven Analysis of Peer-Reviewscited by 16
  11. 2021It's Time to Do Something: Mitigating the Negative Impacts of Computing Through a Change to the Peer Review Processcited by 11
  12. 2019Textual analysis of artificial intelligence manuscripts reveals features associated with peer review outcomecited by 5
4 more
  1. 2022Which structure of academic articles do referees pay more attention to?: perspective of peer review and full-text of academic articlescited by 5
  2. 2025Are the confidence scores of reviewers consistent with the review content? Evidence from top conference proceedings in AIcited by 4
  3. 2022Exploratory analysis of text duplication in peer-review reveals peer-review fraud and paper millscited by 2
  4. 2015Review times in peer review: quantitative analysis of editorial workflowscited by 1

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