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

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Bibliographic Details
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]
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Database: Engineering Source
Description
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]
ISSN:01389130
DOI:10.1007/s11192-025-05446-4