Citation count prediction using non-technical terms in abstracts

Takahiro Baba, Kensuke Baba

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)


Researchers are required to find previous literature which is related to their research and has a scientific impact efficiently from a large number of publications. The target problem of this paper is predicting the citation count of each scholarly paper, that is, the number of citations from other scholarly papers, as the scientific impact. The authors tried to detect the high and low of the citation count of scholarly papers using only their abstracts, especially, non-technical terms used in them. They conducted a classification of abstracts of scholarly papers with high and low citation counts, and applied the classification also to the abstracts modified by deleting technical terms from them. The results of their experiments indicate that the scientific impact of a scholarly paper can be detected from information which is written in its abstract and is not related to the trend of research topics. The classification accuracy for detecting scholarly papers with the top or bottom 1% citation counts was 0.93, and that using the abstracts without technical terms was 0.90.

Original languageEnglish
Title of host publicationComputational Science and Its Applications – ICCSA 2018 - 18th International Conference, 2018, Proceedings
EditorsElena Stankova, Ana Maria Rocha, David Taniar, Osvaldo Gervasi, Eufemia Tarantino, Sanjay Misra, Bernady O. Apduhan, Yeonseung Ryu, Beniamino Murgante, Carmelo M. Torre
PublisherSpringer Verlag
Number of pages10
ISBN (Print)9783319951614
Publication statusPublished - 2018
Externally publishedYes
Event18th International Conference on Computational Science and Its Applications, ICCSA 2018 - Melbourne, Australia
Duration: Jul 2 2018Jul 5 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10960 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other18th International Conference on Computational Science and Its Applications, ICCSA 2018


  • Citation count prediction
  • Document classification
  • Machine learning
  • Text analysis

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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