Toward potential hybrid features evaluation using MLP-ANN binary classification model to tackle meaningful citations

Faiza Qayyum (), Harun Jamil (), Naeem Iqbal (), DoHyeun Kim () and Muhammad Tanvir Afzal ()
Additional contact information
Faiza Qayyum: Jeju National University
Harun Jamil: Jeju National University
Naeem Iqbal: Jeju National University
DoHyeun Kim: Jeju National University
Muhammad Tanvir Afzal: Shifa Tameer-e-Milat University

Scientometrics, 2022, vol. 127, issue 11, No 21, 6499 pages

Abstract: Abstract Citation analysis-based systems are premised on assuming that all citations are equally important. The scientific community argues that a citation may hold divergent reasons and thus, should not be treated at par. In this regard, a plethora of existing studies classifies citations for varying reasons. Presently, the community has a propensity toward binary citation classification with the notion of contemplating only important reasons while employing quantitative analysis-based measures. We argue that outcomes yielded by the contemporary state-of-the-art models cannot be deemed ideal as the plethora of them has been evaluated on a data set with minimal number of instances due to which the outcomes cannot be generalized. The scope of results from such approaches is restricted to a single domain only which may exhibit entirely different behavior for the different data sets. Most of the studies are ruled by the content based features evaluated by harnessing traditional classification models like Support Vector Machine (SVM), and random forest (RF), while an inconsiderable number of studies employ metadata which holds the potential to serve as a quintessential indicator to tackle meaningful citations. In this study, we introduce Multilayer perceptron artificial neural network (MLP-ANN) binary citation classifier, which exploits the best combinations of features formed using both sources. We also introduce a new benchmark data set from the electrical engineering domain which is consolidated with two existing benchmark data sets for model evaluation. The outcomes reveal that the results produced by the proposed MLP model outperform the contemporary models achieving a precision of 0.92.

Keywords: Binary Classification; Citation Count; Information Retrieval; Logistic Regression; Multi-layer Perceptron; Na¨ıve Bayes; Support vector machine (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s11192-022-04530-3 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:scient:v:127:y:2022:i:11:d:10.1007_s11192-022-04530-3

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11192

DOI: 10.1007/s11192-022-04530-3

Access Statistics for this article

Scientometrics is currently edited by Wolfgang Glänzel

More articles in Scientometrics from Springer, Akadémiai Kiadó
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().