Innovation Measurement for Scientific Communication (IMSC) in the Era of Big Data
Science innovation and breakthroughs are the key driving force for scientific and technological advances. Measuring and tracking these innovative studies is an essential direction in informetrics and the science of science. In the big data era, the development of informatics is full of risks and opportunities. A large number of papers is published, but it is challenging to automatically identify and track innovative publications because of their scarcity. Advancement of artificial intelligence, in particular natural language processing and knowledge reasoning, provides potential solutions, using large-scale pre-trained language models, knowledge graphs based on scholarly communication and using full-text analytics to extract and evaluate knowledge entities [4,5]. As stated in the Leiden Manifesto , quantitative evaluation should support qualitative, expert assessment. Both qualitative and quantitative methods have been proposed to measure the innovativeness of knowledge in science communication. For example, Zhang et al. used network analysis to characterize emerging technologies ; Luo et al. measured the originality or novelty of scholarly articles based on combinatorial novelty theory ; Savov et al. applied topic models and bibliometric features to identify highly cited papers or breakthrough papers . Meanwhile, Wu et al. proposed a revised architecture, enhanced hardware, and software infrastructure for the digital library to manage scholarly big data . Identifying the main factors of science and developing predictive models to capture its evolution provides a broader perspective on measuring the innovative nature of science.
This Special Issue aims to bring researchers and practitioners in different disciplines together to understand the challenges, investigate the problems, propose theories, exchange ideas, share resources, and look for new research directions in the field of IMSC. This Special Issue focuses on developing new theories, techniques, solutions, and applications for innovation measurement, as well as the role of innovation measurement in enhancing scientific communication. The Special Issue endeavors to publish research and practice on how to identify and evaluate innovation in different disciplines and contexts, the practices of different machine learning and deep learning techniques for innovation measurement, and the applications of innovation measurement for scientific communication.
List of topic areas
Indicative list of anticipated article topics:
Innovation theory for research contribution identification in academic papers
Evaluation of the technological innovation performance of research organizations
Evaluation of the innovation management ability of research organizations
Theory and framework for innovation measurement
High-quality and reusable dataset for knowledge extraction and innovation measurement
Innovation measurement with natural language processing, machine learning (including deep learning)
Innovation measurement with other related techniques
Knowledge element extraction from academic big data
Feasible and effective evaluation metrics for innovation measurement
Radical and incremental innovation measurement
Multi-information fusion for innovation measurement
Applications of innovation measurement for scientific communication
Innovation diffusion models for scientific communication
Innovation evolution models for scientific communication
Interdisciplinary radical innovation measurement
Interdisciplinary radical innovation diffusion for innovation measurement
The science of science
Team science and innovation
Other related topics
Dr. Zhongyi Wang
Associate Professor at the School of Information Management, Central China Normal University - China
His current research interests include knowledge extraction, science of science, knowledge organization, and retrieval. He serves as PC member of several international conferences and as the reviewer for 8 peer reviewed journals in information science and computer science. He has published more than 50 peer-reviewed papers.
Dr. Haihua Chen
Associate Professor in Data Science in the Department of Information Science, University of North Texas, Denton, Texas - USA
He has been involved in several NSF funded projects. He has expertise in applied data science, natural language processing, information retrieval, and text mining. He co-authored more than 40 publications in academic venues in both information science and computer science. Dr. Chen is serving as co-editor for The Electronic Library, the Special Issue Guest Editor of both the Information Discovery & Delivery and Frontiers in Big Data SIs, the organizing committee of JCDL 2018, IEEE’s AITest 2023, the ISKO international conference 2024, and several workshops. He is the reviewer for 15 peer reviewed journals and several international conferences.
Dr. Chengzhi Zhang
Professor of the Department of Information Management, Nanjing University of Science and Technology, Nanjing, Jiangsu - China
His current research interests include scientific text mining, knowledge entity extraction and evaluation, and social media mining. He serves as an Editorial Board Member and Managing Guest Editor for 10 international journals (Patterns, Heliyon, IP&M, OIR, Aslib JIM, TEL, JDIS, DIM, DI, etc.) and as a PC member of several international conferences in fields of natural language processing and scientometrics.
Dr. Wei Lu
Professor at the School of Information Management and the Director of the Information Retrieval and Knowledge Mining Center, Wuhan University, Wuhan, Hubei - China
He received his PhD degree in Information Science from Wuhan University. His current research interests include information retrieval, text mining, and QA. He has papers published in SIGIR, Information Sciences, JASIST, and IP&M. He serves in diverse roles (e.g., Associate Editor, Editorial Board Member, and Managing Guest Editor) for several journals.
Dr. Jian Wu
Assistant Professor of Computer Science in the Department of Computer Science, Old Dominion University, Norfolk, Virginia - USA
His research interests include natural language processing and understanding, scholarly big data, information retrieval, digital libraries, and the science of science. He has published 70 peer-reviewed papers in ACM, IEEE, and AAAI venues, with best papers and nominations. He was rated the best reviewer in JCDL 2018.
Submissions start: 30 June 2023
Submissions deadline: 15 Feb 2024
Publication in the journal: July 2024
 Diana Hicks, Paul Wouters, Ludo Waltman, Sarah De Rijcke, and Ismael Rafols. 2015. Bibliometrics: the Leiden Manifesto for research metrics. Nature 520, 7548 (2015), 429–431.
 Zhuoran Luo, Wei Lu, Jiangen He, and Yuqi Wang. 2022. Combination of research questions and methods: A new measurement of scientific novelty. Journal of Informetrics 16, 2 (2022), 101282.
 Pavel Savov, Adam Jatowt, and Radoslaw Nielek. 2020. Identifying breakthrough scientific papers. Information Processing & Management 57, 2 (2020), 102168.
 Yuzhuo Wang and Chengzhi Zhang. 2020. Using the full-text content of academic articles to identify and evaluate algorithm entities in the domain of natural language processing. Journal of informetrics 14, 4 (2020), 101091.
 Zhongyi Wang, Keying Wang, Jiyue Liu, Jing Huang, and Haihua Chen. 2022. Measuring the innovation of method knowledge elements in scientific literature. Scientometrics 127, 5 (2022), 2803–2827.
 Jian Wu, Shaurya Rohatgi, Sai Raghav Reddy Keesara, Jason Chhay, Kevin Kuo, Arjun Manoj Menon, Sean Parsons, Bhuvan Urgaonkar, and C Lee Giles. 2021. Building an Accessible, Usable, Scalable, and Sustainable Service for Scholarly Big Data. In 2021 IEEE International Conference on Big Data (Big Data). IEEE, 141–152.
 Yi Zhang, Mengjia Wu, Wen Miao, Lu Huang, and Jie Lu. 2021. Bi-layer network analytics: A methodology for characterizing emerging general-purpose technologies. Journal of Informetrics 15, 4 (2021), 10120.