User Motivation Classification and Comparison of Tweets Mentioning Research Articles in the Fields of Medicine, Chemistry and Environmental Science

Mahalakshmi Suresh Kumar; Shreya Gupta; Subashini Baskaran; Jin Cheon Na

doi:10.1007/978-3-030-34058-2_5

User Motivation Classification and Comparison of Tweets Mentioning Research Articles in the Fields of Medicine, Chemistry and Environmental Science

Mahalakshmi Suresh Kumar, Shreya Gupta, Subashini Baskaran^*, Jin Cheon Na

^*Corresponding author for this work

Nanyang Technological University

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

3 Citations (Scopus)

Abstract

Modern metrics like Altmetrics help researchers and scientists to gauge the impact of their research findings through social media discussions. Twitter holds more scholarly and scientific discussions than other social media platforms and is extensively used to discuss and share research articles by domain experts as well as by the general public. In this study, we have analyzed the motivations of people using Twitter as a medium to propagate the research works. Tweets and the publication details from the field of medicine are collected from altmetric.com for journals with high impact factors and a Support Vector Machine classifier is developed with 85.2% accuracy to categorize the tweets into one of the six motivation classes. The model is then extended to observe the pattern of user motivations in chemistry and environmental science. Medicine and environmental science were found to have similar patterns in user motivations as they directly impact the general public. Chemistry, on the other hand, showed a peculiar pattern with a high percentage of self-citation and promotion. From this study, the domain is also found to play a vital role in measuring research impacts when alternate metrics are used.

Original language	English
Title of host publication	Digital Libraries at the Crossroads of Digital Information for the Future - 21st International Conference on Asia-Pacific Digital Libraries, ICADL 2019, Proceedings
Editors	Adam Jatowt, Akira Maeda, Sue Yeon Syn
Publisher	Springer
Pages	40-53
Number of pages	14
ISBN (Print)	9783030340575
DOIs	https://doi.org/10.1007/978-3-030-34058-2_5
Publication status	Published - 2019
Externally published	Yes
Event	21st International Conference on Asia-Pacific Digital Libraries, ICADL 2019 - Kuala Lumpur, Malaysia Duration: Nov 4 2019 → Nov 7 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11853 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	21st International Conference on Asia-Pacific Digital Libraries, ICADL 2019
Country/Territory	Malaysia
City	Kuala Lumpur
Period	11/4/19 → 11/7/19

Bibliographical note

Publisher Copyright:
© Springer Nature Switzerland AG, 2019.

ASJC Scopus Subject Areas

Theoretical Computer Science
General Computer Science

Keywords

Altmetrics
Chemistry
Environmental science
Machine learning
Medicine
Support Vector Machine
Twitter
User motivation

Access to Document

10.1007/978-3-030-34058-2_5

Cite this

Kumar, M. S., Gupta, S., Baskaran, S., & Na, J. C. (2019). User Motivation Classification and Comparison of Tweets Mentioning Research Articles in the Fields of Medicine, Chemistry and Environmental Science. In A. Jatowt, A. Maeda, & S. Y. Syn (Eds.), Digital Libraries at the Crossroads of Digital Information for the Future - 21st International Conference on Asia-Pacific Digital Libraries, ICADL 2019, Proceedings (pp. 40-53). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11853 LNCS). Springer. https://doi.org/10.1007/978-3-030-34058-2_5

Kumar, Mahalakshmi Suresh ; Gupta, Shreya ; Baskaran, Subashini et al. / User Motivation Classification and Comparison of Tweets Mentioning Research Articles in the Fields of Medicine, Chemistry and Environmental Science. Digital Libraries at the Crossroads of Digital Information for the Future - 21st International Conference on Asia-Pacific Digital Libraries, ICADL 2019, Proceedings. editor / Adam Jatowt ; Akira Maeda ; Sue Yeon Syn. Springer, 2019. pp. 40-53 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{cae1ef77a5dc41f4b65183c8a5b8d91c,

title = "User Motivation Classification and Comparison of Tweets Mentioning Research Articles in the Fields of Medicine, Chemistry and Environmental Science",

abstract = "Modern metrics like Altmetrics help researchers and scientists to gauge the impact of their research findings through social media discussions. Twitter holds more scholarly and scientific discussions than other social media platforms and is extensively used to discuss and share research articles by domain experts as well as by the general public. In this study, we have analyzed the motivations of people using Twitter as a medium to propagate the research works. Tweets and the publication details from the field of medicine are collected from altmetric.com for journals with high impact factors and a Support Vector Machine classifier is developed with 85.2% accuracy to categorize the tweets into one of the six motivation classes. The model is then extended to observe the pattern of user motivations in chemistry and environmental science. Medicine and environmental science were found to have similar patterns in user motivations as they directly impact the general public. Chemistry, on the other hand, showed a peculiar pattern with a high percentage of self-citation and promotion. From this study, the domain is also found to play a vital role in measuring research impacts when alternate metrics are used.",

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author = "Kumar, {Mahalakshmi Suresh} and Shreya Gupta and Subashini Baskaran and Na, {Jin Cheon}",

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year = "2019",

doi = "10.1007/978-3-030-34058-2_5",

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Kumar, MS, Gupta, S, Baskaran, S & Na, JC 2019, User Motivation Classification and Comparison of Tweets Mentioning Research Articles in the Fields of Medicine, Chemistry and Environmental Science. in A Jatowt, A Maeda & SY Syn (eds), Digital Libraries at the Crossroads of Digital Information for the Future - 21st International Conference on Asia-Pacific Digital Libraries, ICADL 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11853 LNCS, Springer, pp. 40-53, 21st International Conference on Asia-Pacific Digital Libraries, ICADL 2019, Kuala Lumpur, Malaysia, 11/4/19. https://doi.org/10.1007/978-3-030-34058-2_5

User Motivation Classification and Comparison of Tweets Mentioning Research Articles in the Fields of Medicine, Chemistry and Environmental Science. / Kumar, Mahalakshmi Suresh; Gupta, Shreya; Baskaran, Subashini et al.
Digital Libraries at the Crossroads of Digital Information for the Future - 21st International Conference on Asia-Pacific Digital Libraries, ICADL 2019, Proceedings. ed. / Adam Jatowt; Akira Maeda; Sue Yeon Syn. Springer, 2019. p. 40-53 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11853 LNCS).

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

TY - GEN

T1 - User Motivation Classification and Comparison of Tweets Mentioning Research Articles in the Fields of Medicine, Chemistry and Environmental Science

AU - Kumar, Mahalakshmi Suresh

AU - Gupta, Shreya

AU - Baskaran, Subashini

AU - Na, Jin Cheon

N1 - Publisher Copyright: © Springer Nature Switzerland AG, 2019.

PY - 2019

Y1 - 2019

N2 - Modern metrics like Altmetrics help researchers and scientists to gauge the impact of their research findings through social media discussions. Twitter holds more scholarly and scientific discussions than other social media platforms and is extensively used to discuss and share research articles by domain experts as well as by the general public. In this study, we have analyzed the motivations of people using Twitter as a medium to propagate the research works. Tweets and the publication details from the field of medicine are collected from altmetric.com for journals with high impact factors and a Support Vector Machine classifier is developed with 85.2% accuracy to categorize the tweets into one of the six motivation classes. The model is then extended to observe the pattern of user motivations in chemistry and environmental science. Medicine and environmental science were found to have similar patterns in user motivations as they directly impact the general public. Chemistry, on the other hand, showed a peculiar pattern with a high percentage of self-citation and promotion. From this study, the domain is also found to play a vital role in measuring research impacts when alternate metrics are used.

AB - Modern metrics like Altmetrics help researchers and scientists to gauge the impact of their research findings through social media discussions. Twitter holds more scholarly and scientific discussions than other social media platforms and is extensively used to discuss and share research articles by domain experts as well as by the general public. In this study, we have analyzed the motivations of people using Twitter as a medium to propagate the research works. Tweets and the publication details from the field of medicine are collected from altmetric.com for journals with high impact factors and a Support Vector Machine classifier is developed with 85.2% accuracy to categorize the tweets into one of the six motivation classes. The model is then extended to observe the pattern of user motivations in chemistry and environmental science. Medicine and environmental science were found to have similar patterns in user motivations as they directly impact the general public. Chemistry, on the other hand, showed a peculiar pattern with a high percentage of self-citation and promotion. From this study, the domain is also found to play a vital role in measuring research impacts when alternate metrics are used.

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KW - Medicine

KW - Support Vector Machine

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KW - User motivation

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SN - 9783030340575

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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Kumar MS, Gupta S, Baskaran S, Na JC. User Motivation Classification and Comparison of Tweets Mentioning Research Articles in the Fields of Medicine, Chemistry and Environmental Science. In Jatowt A, Maeda A, Syn SY, editors, Digital Libraries at the Crossroads of Digital Information for the Future - 21st International Conference on Asia-Pacific Digital Libraries, ICADL 2019, Proceedings. Springer. 2019. p. 40-53. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-34058-2_5

User Motivation Classification and Comparison of Tweets Mentioning Research Articles in the Fields of Medicine, Chemistry and Environmental Science

Abstract

Publication series

Conference

Bibliographical note

ASJC Scopus Subject Areas

Keywords

Access to Document

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Cite this