Published May 21, 2020 | Version v1
Dataset Open

PredCheck: Detecting Predatory Behaviour in Scholarly World

  • 1. IIT Gandhinagar

Description

Dataset used in the paper "PredCheck: Detecting Predatory Behaviour in Scholarly World" accepted at JCDL 2020 as a poster.

Abstract: High solicitation for publishing a paper in scientific journals has led to the emergence of a large number of open-access predatory publishers. They fail to provide a rigorous peer-review process, thereby diluting the quality of research work and charge high article processing fees. Identification of such publishers has remained a challenge due to the vast diversity of the scholarly publishing ecosystem. Earlier works utilises only the objective features such as metadata. In this work, we aim to explore the possibility of identifying predatory behaviour through text-based features. We propose PredCheck, a four-step classificaton pipeline. The first classifier identifies the subject of the paper using TF-IDF vectors. Based on the subject of the paper, the Doc2Vec embeddings of the text are found. These embeddings are then fed into a Naive Bayes classifier that identifies the text to be predatory or non-predatory. Our pipeline gives a macro accuracy of 95% and an F1-score of 0.89.

Files

Biomed-BMC.zip

Files (733.4 MB)

Name Size Download all
md5:41eff7f6c79b616dac5b6e34d119e0de
534.6 MB Preview Download
md5:0bd9fb62b1498540fe20369e0babcb96
5.9 MB Preview Download
md5:532fe9966aa6e713563233b778e15264
74.6 MB Preview Download
md5:f2c51235f030890e1b8cd40732c41be6
7.3 MB Preview Download
md5:48874e17b84db93b3e35f726eda9631f
14.0 MB Preview Download
md5:c07b2ff10b89cde947c85a99792f9124
25.9 MB Preview Download
md5:48874e17b84db93b3e35f726eda9631f
14.0 MB Preview Download
md5:2d07e08e4b09ba9197107526c9b1664d
31.6 MB Preview Download
md5:ea3f06f1ad71270a4214d86dbc491167
25.5 MB Preview Download

Additional details

Related works

Is documented by
Conference paper: https://dl.acm.org/doi/10.1145/3383583.3398593 (URL)