Characterising authors on the extent of their paper acceptance: A case study of the Journal of High Energy Physics

TL;DR

This study analyzes author profiles, peer review texts, and network features at the Journal of High Energy Physics to identify factors associated with high paper acceptance rates, revealing significant correlations and predictive models.

Contribution

It introduces a comprehensive analysis combining author metrics, review characteristics, and network features to predict paper acceptance, highlighting potential systemic biases.

Findings

High acceptance authors have more citations, higher h-index, and more collaborators.

Accepted papers receive longer, more positive reviews.

Machine learning models achieve high precision and recall in predicting acceptance.

Abstract

New researchers are usually very curious about the recipe that could accelerate the chances of their paper getting accepted in a reputed forum (journal/conference). In search of such a recipe, we investigate the profile and peer review text of authors whose papers almost always get accepted at a venue (Journal of High Energy Physics in our current work). We find authors with high acceptance rate are likely to have a high number of citations, high $h$-index, higher number of collaborators etc. We notice that they receive relatively lengthy and positive reviews for their papers. In addition, we also construct three networks -- co-reviewer, co-citation and collaboration network and study the network-centric features and intra- and inter-category edge interactions. We find that the authors with high acceptance rate are more `central' in these networks; the volume of intra- and inter-category interactions are also drastically different for the authors with high acceptance rate compared to the other authors. Finally, using the above set of features, we train standard machine learning models (random forest, XGBoost) and obtain very high class wise precision and recall. In a followup discussion we also narrate how apart from the author characteristics, the peer-review system might itself have a role in propelling the distinction among the different categories which could lead to potential discrimination and unfairness and calls for further investigation by the system admins.