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First to know: profiling readers of early-access articles in the field of AI.

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Title: First to know: profiling readers of early-access articles in the field of AI.
Authors: Xu, Yi-Shuai1 (AUTHOR), Yanti Idaya, A. M. K.1 (AUTHOR) yanti@um.edu.my, Kassim, Muhammad Shahreeza Safiruz2 (AUTHOR)
Source: Scientometrics. Nov2025, Vol. 130 Issue 11, p6747-6774. 28p.
Subjects: Artificial intelligence, Readership, University faculty, Citation analysis, Educational background, Publishing, Interdisciplinary approach to knowledge, Information sharing
Abstract: Artificial intelligence (AI) research has grown rapidly in recent years, reflected in the substantial increase of AI-related publications indexed in major databases. This rapid expansion has intensified the demand for timely dissemination mechanisms, highlighting the importance of Early Access (EA) publication as a channel for accelerating knowledge exchange. However, limited research has examined the readership characteristics and dissemination patterns of AI articles published as Early Access (AI-EA). This study investigates the academic roles and disciplinary backgrounds of AI-EA readers, examines role-based and disciplinary reader networks, and analyzes the relationship between reader counts and the citation performance of these articles. We constructed a dataset of 3364 AI-EA articles published between 2024 and 2025 in the Web of Science Core Collection. Using the Mendeley API, we retrieved metadata and reader statistics through DOI matching, achieving a 93% coverage rate. In total, 3128 articles attracted 16,652 readers, with demographic data available for 7487 of them. PhD (21.09%) and Master (14.41%) led the student group, while researchers (20.57%) and lecturers (10.47%) dominated the faculty readership. Disciplinary analysis shows that most readers were from computer science (42.49%) and engineering (19.43%), with significant engagement also observed from the social sciences (17.80%), life sciences (7.59%), natural sciences (5.76%), and even arts and humanities (4.55%). Co-occurrence network reveals strong clustering among technical disciplines, alongside meaningful interdisciplinary links. Role-based network analysis identifies graduate students, researchers, and lecturers as key diffusion nodes. A weak but statistically significant Spearman correlation (ρ = 0.209, p < 0.001) between reader counts and citations suggests that early readership may provide predictive signals of future academic impact. These findings offer new insights into early-stage scholarly interaction with AI-EA and inform more targeted dissemination strategies. [ABSTRACT FROM AUTHOR]
Copyright of Scientometrics is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: First to know: profiling readers of early-access articles in the field of AI.
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  Data: &lt;searchLink fieldCode=&quot;JN&quot; term=&quot;%22Scientometrics%22&quot;&gt;Scientometrics&lt;/searchLink&gt;. Nov2025, Vol. 130 Issue 11, p6747-6774. 28p.
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  Data: Artificial intelligence (AI) research has grown rapidly in recent years, reflected in the substantial increase of AI-related publications indexed in major databases. This rapid expansion has intensified the demand for timely dissemination mechanisms, highlighting the importance of Early Access (EA) publication as a channel for accelerating knowledge exchange. However, limited research has examined the readership characteristics and dissemination patterns of AI articles published as Early Access (AI-EA). This study investigates the academic roles and disciplinary backgrounds of AI-EA readers, examines role-based and disciplinary reader networks, and analyzes the relationship between reader counts and the citation performance of these articles. We constructed a dataset of 3364 AI-EA articles published between 2024 and 2025 in the Web of Science Core Collection. Using the Mendeley API, we retrieved metadata and reader statistics through DOI matching, achieving a 93% coverage rate. In total, 3128 articles attracted 16,652 readers, with demographic data available for 7487 of them. PhD (21.09%) and Master (14.41%) led the student group, while researchers (20.57%) and lecturers (10.47%) dominated the faculty readership. Disciplinary analysis shows that most readers were from computer science (42.49%) and engineering (19.43%), with significant engagement also observed from the social sciences (17.80%), life sciences (7.59%), natural sciences (5.76%), and even arts and humanities (4.55%). Co-occurrence network reveals strong clustering among technical disciplines, alongside meaningful interdisciplinary links. Role-based network analysis identifies graduate students, researchers, and lecturers as key diffusion nodes. A weak but statistically significant Spearman correlation (ρ = 0.209, p &lt; 0.001) between reader counts and citations suggests that early readership may provide predictive signals of future academic impact. These findings offer new insights into early-stage scholarly interaction with AI-EA and inform more targeted dissemination strategies. [ABSTRACT FROM AUTHOR]
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  Data: &lt;i&gt;Copyright of Scientometrics is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder&#39;s express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.&lt;/i&gt; (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=189803016
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        Value: 10.1007/s11192-025-05446-4
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      – Code: eng
        Text: English
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      – SubjectFull: Artificial intelligence
        Type: general
      – SubjectFull: Readership
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      – SubjectFull: University faculty
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      – SubjectFull: Citation analysis
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      – SubjectFull: Educational background
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      – SubjectFull: Publishing
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      – SubjectFull: Interdisciplinary approach to knowledge
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      – TitleFull: First to know: profiling readers of early-access articles in the field of AI.
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              Text: Nov2025
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