Title
Characterising authors on the extent of their paper acceptance: A case study of the Journal of High Energy Physics
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.
Year
DOI
Venue
2020
10.1145/3383583.3398527
JCDL '20: The ACM/IEEE Joint Conference on Digital Libraries in 2020 Virtual Event China August, 2020
DocType
ISBN
Citations 
Conference
978-1-4503-7585-6
0
PageRank 
References 
Authors
0.34
9
5
Name
Order
Citations
PageRank
Hazra Rima100.34
Aryan200.34
Aggarwal Hardik300.34
Matteo Marsili414917.65
Animesh Mukherjee539262.78