A hybrid human-AI tool for scientometric analysis
A hybrid human-AI tool for scientometric analysis
Solid research depends on systematic, verifiable and repeatable scientometric analysis. However, scientometric analysis is difficult in the current research landscape characterized by the increasing number of publications per year, intersections between research domains, and the diversity of stakeholders involved in research projects. To address this problem, we propose SciCrowd, a hybrid human–AI mixed-initiative system, which supports the collaboration between Artificial Intelligence services and crowdsourcing services. This work discusses the design and evaluation of SciCrowd. The evaluation is focused on attitudes, concerns and intentions towards use. This study contributes a nuanced understanding of the interplay between algorithmic and human tasks in the process of conducting scientometric analysis.
Artificial intelligence, Bibliometric-enhanced information retrieval, Crowdsourcing, Human–AI interaction, Reinforcement learning from human feedback, Scientometrics
983-1010
Correia, Antonio
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Grover, Andrea
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Jameel, Shoaib
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Schneider, Daniel
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Antunes, Pedro
ced6af76-121d-42f9-9eaa-b672e8c88ed2
Fonseca, Benjamin
b078c6cf-f9c9-4ec6-bd42-3a29533383a2
October 2023
Correia, Antonio
c89b4cba-4ff1-423e-94fa-508e50bfb026
Grover, Andrea
a4f1e343-088b-4a3e-b843-849e7f2371d6
Jameel, Shoaib
ae3c588e-4a59-43d9-af41-ea30d7caaf96
Schneider, Daniel
65fc7332-111a-4084-842c-1cb3f955204a
Antunes, Pedro
ced6af76-121d-42f9-9eaa-b672e8c88ed2
Fonseca, Benjamin
b078c6cf-f9c9-4ec6-bd42-3a29533383a2
Correia, Antonio, Grover, Andrea, Jameel, Shoaib, Schneider, Daniel, Antunes, Pedro and Fonseca, Benjamin
(2023)
A hybrid human-AI tool for scientometric analysis.
Artificial Intelligence Review, 56, .
(doi:10.1007/s10462-023-10548-7).
Abstract
Solid research depends on systematic, verifiable and repeatable scientometric analysis. However, scientometric analysis is difficult in the current research landscape characterized by the increasing number of publications per year, intersections between research domains, and the diversity of stakeholders involved in research projects. To address this problem, we propose SciCrowd, a hybrid human–AI mixed-initiative system, which supports the collaboration between Artificial Intelligence services and crowdsourcing services. This work discusses the design and evaluation of SciCrowd. The evaluation is focused on attitudes, concerns and intentions towards use. This study contributes a nuanced understanding of the interplay between algorithmic and human tasks in the process of conducting scientometric analysis.
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A hybrid human-AI tool for scientometric analysis
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Accepted/In Press date: 1 July 2023
e-pub ahead of print date: 12 July 2023
Published date: October 2023
Additional Information:
Funding Information:
This research was mainly performed during an internship of António Correia at Microsoft Research, Cambridge, UK. The work was supported in part by the Portuguese Foundation for Science and Technology (FCT), national funding through the individual research Grant SFRH/BD/136211/2018. The authors would like to thank Siân Lindley from Microsoft Research for the important role in understanding and modifying the human–AI scientometric workflow that supports the SciCrowd system, as well as Jorge Santos for the help while building the necessary infrastructure. Our thanks extend to Hugo Paredes for the helpful discussions and valuable insights in the early stages of this work.
Funding Information:
This research was mainly performed during an internship of António Correia at Microsoft Research, Cambridge, UK. The work was supported in part by the Portuguese Foundation for Science and Technology (FCT), national funding through the individual research Grant SFRH/BD/136211/2018. The authors would like to thank Siân Lindley from Microsoft Research for the important role in understanding and modifying the human–AI scientometric workflow that supports the SciCrowd system, as well as Jorge Santos for the help while building the necessary infrastructure. Our thanks extend to Hugo Paredes for the helpful discussions and valuable insights in the early stages of this work.
Publisher Copyright:
© 2023, The Author(s), under exclusive licence to Springer Nature B.V.
Keywords:
Artificial intelligence, Bibliometric-enhanced information retrieval, Crowdsourcing, Human–AI interaction, Reinforcement learning from human feedback, Scientometrics
Identifiers
Local EPrints ID: 482290
URI: http://eprints.soton.ac.uk/id/eprint/482290
ISSN: 0269-2821
PURE UUID: 8c1a8ec4-9046-4efa-a410-4e3a8fc374a1
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Date deposited: 26 Sep 2023 16:36
Last modified: 17 Mar 2024 04:44
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Contributors
Author:
Antonio Correia
Author:
Andrea Grover
Author:
Shoaib Jameel
Author:
Daniel Schneider
Author:
Pedro Antunes
Author:
Benjamin Fonseca
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