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Extracting scientific trends by mining topics from Call for Papers

Noor Arshad (Information Technology University, Lahore, Pakistan)
Abu Bakar (Information Technology University, Lahore, Pakistan)
Saira Hanif Soroya (University of the Punjab, Lahore, Pakistan)
Iqra Safder (Information Technology University, Lahore, Pakistan)
Sajjad Haider (Capital University of Science & Technology, Islamabad, Pakistan)
Saeed-Ul Hassan (Department of Computer Science, Information Technology University, Lahore, Pakistan)
Naif Radi Aljohani (Faculty of Computing and Information Technology, Jeddah, Saudi Arabia)
Salem Alelyani (Center for Artificial Intelligence (CAI), King Khalid University, Abha, Saudi Arabia) (College of Computer Science, King Khalid University, Abha, Saudi Arabia)
Raheel Nawaz (Manchester Metropolitan University, Manchester, UK)

Library Hi Tech

ISSN: 0737-8831

Article publication date: 9 December 2019

Issue publication date: 14 February 2022

376

Abstract

Purpose

The purpose of this paper is to present a novel approach for mining scientific trends using topics from Call for Papers (CFP). The work contributes a valuable input for researchers, academics, funding institutes and research administration departments by sharing the trends to set directions of research path.

Design/methodology/approach

The authors procure an innovative CFP data set to analyse scientific evolution and prestige of conferences that set scientific trends using scientific publications indexed in DBLP. Using the Field of Research code 804 from Australian Research Council, the authors identify 146 conferences (from 2006 to 2015) into different thematic areas by matching the terms extracted from publication titles with the Association for Computing Machinery Computing Classification System. Furthermore, the authors enrich the vocabulary of terms from the WordNet dictionary and Growbag data set. To measure the significance of terms, the authors adopt the following weighting schemas: probabilistic, gram, relative, accumulative and hierarchal.

Findings

The results indicate the rise of “big data analytics” from CFP topics in the last few years. Whereas the topics related to “privacy and security” show an exponential increase, the topics related to “semantic web” show a downfall in recent years. While analysing publication output in DBLP that matches CFP indexed in ERA Core A* to C rank conference, the authors identified that A* and A tier conferences not merely set publication trends, since B or C tier conferences target similar CFP.

Originality/value

Overall, the analyses presented in this research are prolific for the scientific community and research administrators to study research trends and better data management of digital libraries pertaining to the scientific literature.

Keywords

Acknowledgements

This paper has been extended from the poster paper accepted in International Conference of Asian Digital Libraries held in New Zealand in 2018. The authors are grateful for the financial support received from King Khalid University for this research Under Grant No. 239, 2019. This paper forms part of a special section “Informetrics on Social Network Mining: Research, Policy and Practice challenges - Part 2”, guest edited by Mu-Yen Chen, Chien-Hsiang Liao, Edwin David Lughofer and Erol Egrioglu.

Citation

Arshad, N., Bakar, A., Soroya, S.H., Safder, I., Haider, S., Hassan, S.-U., Aljohani, N.R., Alelyani, S. and Nawaz, R. (2022), "Extracting scientific trends by mining topics from Call for Papers", Library Hi Tech, Vol. 40 No. 1, pp. 115-132. https://doi.org/10.1108/LHT-02-2019-0048

Publisher

:

Emerald Publishing Limited

Copyright © 2019, Emerald Publishing Limited

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