Citation count prediction using non-technical terms in abstracts

Takahiro Baba; Kensuke Baba

doi:10.1007/978-3-319-95162-1_25

Citation count prediction using non-technical terms in abstracts

Takahiro Baba, Kensuke Baba

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Citations (Scopus)

Abstract

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 language	English
Title of host publication	Computational Science and Its Applications – ICCSA 2018 - 18th International Conference, 2018, Proceedings
Editors	Elena Stankova, Ana Maria Rocha, David Taniar, Osvaldo Gervasi, Eufemia Tarantino, Sanjay Misra, Bernady O. Apduhan, Yeonseung Ryu, Beniamino Murgante, Carmelo M. Torre
Publisher	Springer Verlag
Pages	366-375
Number of pages	10
ISBN (Print)	9783319951614
DOIs	https://doi.org/10.1007/978-3-319-95162-1_25
Publication status	Published - 2018
Externally published	Yes
Event	18th International Conference on Computational Science and Its Applications, ICCSA 2018 - Melbourne, Australia Duration: Jul 2 2018 → Jul 5 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10960 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	18th International Conference on Computational Science and Its Applications, ICCSA 2018
Country/Territory	Australia
City	Melbourne
Period	7/2/18 → 7/5/18

Keywords

Citation count prediction
Document classification
Machine learning
Text analysis

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-319-95162-1_25

Cite this

Baba, T., & Baba, K. (2018). Citation count prediction using non-technical terms in abstracts. In E. Stankova, A. M. Rocha, D. Taniar, O. Gervasi, E. Tarantino, S. Misra, B. O. Apduhan, Y. Ryu, B. Murgante, & C. M. Torre (Eds.), Computational Science and Its Applications – ICCSA 2018 - 18th International Conference, 2018, Proceedings (pp. 366-375). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10960 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-95162-1_25

Citation count prediction using non-technical terms in abstracts. / Baba, Takahiro; Baba, Kensuke.
Computational Science and Its Applications – ICCSA 2018 - 18th International Conference, 2018, Proceedings. ed. / Elena Stankova; Ana Maria Rocha; David Taniar; Osvaldo Gervasi; Eufemia Tarantino; Sanjay Misra; Bernady O. Apduhan; Yeonseung Ryu; Beniamino Murgante; Carmelo M. Torre. Springer Verlag, 2018. p. 366-375 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10960 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Baba, T & Baba, K 2018, Citation count prediction using non-technical terms in abstracts. in E Stankova, AM Rocha, D Taniar, O Gervasi, E Tarantino, S Misra, BO Apduhan, Y Ryu, B Murgante & CM Torre (eds), Computational Science and Its Applications – ICCSA 2018 - 18th International Conference, 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10960 LNCS, Springer Verlag, pp. 366-375, 18th International Conference on Computational Science and Its Applications, ICCSA 2018, Melbourne, Australia, 7/2/18. https://doi.org/10.1007/978-3-319-95162-1_25

Baba T, Baba K. Citation count prediction using non-technical terms in abstracts. In Stankova E, Rocha AM, Taniar D, Gervasi O, Tarantino E, Misra S, Apduhan BO, Ryu Y, Murgante B, Torre CM, editors, Computational Science and Its Applications – ICCSA 2018 - 18th International Conference, 2018, Proceedings. Springer Verlag. 2018. p. 366-375. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-95162-1_25

Baba, Takahiro ; Baba, Kensuke. / Citation count prediction using non-technical terms in abstracts. Computational Science and Its Applications – ICCSA 2018 - 18th International Conference, 2018, Proceedings. editor / Elena Stankova ; Ana Maria Rocha ; David Taniar ; Osvaldo Gervasi ; Eufemia Tarantino ; Sanjay Misra ; Bernady O. Apduhan ; Yeonseung Ryu ; Beniamino Murgante ; Carmelo M. Torre. Springer Verlag, 2018. pp. 366-375 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "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.",

keywords = "Citation count prediction, Document classification, Machine learning, Text analysis",

author = "Takahiro Baba and Kensuke Baba",

note = "Funding Information: This work was supported by JSPS KAKENHI Grant Number 15K00310.; 18th International Conference on Computational Science and Its Applications, ICCSA 2018 ; Conference date: 02-07-2018 Through 05-07-2018",

year = "2018",

doi = "10.1007/978-3-319-95162-1_25",

language = "English",

isbn = "9783319951614",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "366--375",

editor = "Elena Stankova and Rocha, {Ana Maria} and David Taniar and Osvaldo Gervasi and Eufemia Tarantino and Sanjay Misra and Apduhan, {Bernady O.} and Yeonseung Ryu and Beniamino Murgante and Torre, {Carmelo M.}",

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AU - Baba, Kensuke

N1 - Funding Information: This work was supported by JSPS KAKENHI Grant Number 15K00310.

PY - 2018

Y1 - 2018

N2 - 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.

AB - 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.

KW - Citation count prediction

KW - Document classification

KW - Machine learning

KW - Text analysis

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ER -

Citation count prediction using non-technical terms in abstracts

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