Table of Content

Emerging Techniques on Citation Analysis in Scholarly Articles

Submission Deadline: 06 October 2022 (closed)

Guest Editors

Dr. Muhammad Asif, National Textile University, Pakistan.
Dr. Kemal Polat, Bolu Abant Izzet Baysal University, Turkey.
Dr. Osama Sohaib, University of Technology, Sydney.


In this epoch of massive research findings, each day research corpus is being generated. New discoveries are always based on previously conducted research experiments. Researchers do citations to acknowledge the prior work and its author Citations serves as key factors for measuring the different technical aspects of research work such as ranking the researchers, calculating the impact factor of journals, H-index, I-index, to point out research trends, for allocating research grants and prizes etc. The researchers have argued that only quantitative analysis is not sufficient for measuring the potential aspects of research and suggested to incorporate the reasons for citations for the betterment of analysis. Therefore, both quantitative and qualitative approaches should be considered. In the field of citation analysis, there exist different techniques that are based on Content, Metadata, and Citation count but the results produced to perform this task are insufficient for making potential decisions. To make potential decisions the state-of-the-art results should be improved. This special issue aims to turn the focus of research community towards importance of Citation Analysis.

The following topics are included but not limited to:
• Citation Identification
• Citation Analysis
• Sentiment Analysis on Citations
• Predictive Analysis
• Citation Recommendations 
• Research Paper similarity based on Citations
• Citation Reasoning 
• Citation Classification
• Citations in Recommender Systems
• Qualitative Research Analysis
• Co-citation Analysis
• Citation as scientific measure


Citation Classification, Citation Analysis, Important Citation Identification, Co-Citation Analysis

Published Papers

  • Open Access


    Active Learning Strategies for Textual Dataset-Automatic Labelling

    Sher Muhammad Daudpota, Saif Hassan, Yazeed Alkhurayyif, Abdullah Saleh Alqahtani, Muhammad Haris Aziz
    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1409-1422, 2023, DOI:10.32604/cmc.2023.034157
    (This article belongs to this Special Issue: Emerging Techniques on Citation Analysis in Scholarly Articles)
    Abstract The Internet revolution has resulted in abundant data from various sources, including social media, traditional media, etcetera. Although the availability of data is no longer an issue, data labelling for exploiting it in supervised machine learning is still an expensive process and involves tedious human efforts. The overall purpose of this study is to propose a strategy to automatically label the unlabeled textual data with the support of active learning in combination with deep learning. More specifically, this study assesses the performance of different active learning strategies in automatic labelling of the textual dataset at sentence and document levels. To… More >

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