The increasingly mature artificial intelligence technologies, such as big data, deep learning, and natural language processing, provide technical support for research on automatic text understanding and bring development opportunities for innovative measurement of scientific communication. Innovation measurement in scientific communication is a challenging and cutting-edge direction in Informetrics. It is interdisciplinary, requiring considering the characteristics of different disciplines and different types of scientific outcomes to establish a comprehensive evaluation metrics system. On the other hand, metadata and content features should be considered to reflect the innovation of scientific works objectively and comprehensively. This workshop focuses on the discussion and produces enlightening outcomes. We will engage broad audiences to share their ideas and pre-productions, enabling an interdisciplinary approach to exploring frontier areas. This workshop consists of keynotes, oral presentations, and poster sessions and would attract interest from academic researchers, librarians, and decision-makers from governments and practical sectors.

Submission Guidelines

All papers must be original and not simultaneously submitted to another journal or conference. The following paper categories are welcome:

• Regular papers: All submissions must be written in English, following the ACM Proceedings template (10 pages for full papers and 4 pages for short papers exclusive of unlimited pages for references).
• Poster & demonstration: We welcome detailed originality, early discoveries, work-in-progress and industrial applications of innovations in measurement science communication to be presented in special poster sessions, and possibly in 2-minute presentations in the main session.

Committees

Program Committee

• Angelo Antonio, The Open University

• Hsia-Ching Chang, University of North Texas

• Sijing Chen, Central China Normal University

• Orduna-Malea Enrique, Universitat Politècnica de València

• Jiangen He, University of Tennessee at Knoxville

• Linlin Hou, Nankai University

• Xiao Huang, Central China Normal University

• Ying Huang, Wuhan University

• Jiang Li, Nanjing University

• Mauro Dalle Lu, Universidade de Campinas

• Jin Mao, Wuhan University

• Chao Min, Nanjing University

• Dongin Nam, Yonsei University

• Yan Qi, Institute of Medical Information / Medical Library. CAMS & PUMC

• Feng Shi, The University of North Carolina at Chapel Hill

• Mathias Tessma, Brazilian Institute of Education

• Yang Wang, Xi'an Jiao Tong University

• Haiyun Xu, Shandong University of Technology

• Shuo Xu, Beijing University of Technology

• Jinzhu Zhang, Nanjing University of Science and Technology

• Huanhuan Zhao, University of Tennessee at Knoxville

Organizing committee

• Zhongyi Wang is an associate professor of 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 members of several international conferences and the reviewer for 8 peer reviewed journals in information science and computer science. He has published more than 50 peer-reviewed papers.
• Haihua Chen is an assistant professor in Data Science in the Department of Information Science at the University of North Texas. 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 guest editor of Information Discovery & Delivery and Frontiers in Big Data special issues, the organizing committee of JCDL 2018, IEEE AITest 2023, ISKO international conference 2024, and several workshops. He is the reviewer for 14 peer reviewed journals and several international conferences.
• Chengzhi Zhang is a professor of Department of Information Management, Nanjing University of Science and Technology, China. His current research interests include scientific text mining, knowledge entity extraction and evaluation, social media mining. He serves as Editorial Board Member and Managing Guest Editor for 10 international journals (Patterns, IP&M, JOI, Scientometrics, OIR, Aslib JIM, TEL, JDIS, DIM, DI, etc.) and PC members of several international conferences in fields of natural language process and scientometrics.
• Yi Bu is an assistant professor at the Department of Information Management, Peking University, China. He earned a B.Mngt. in Information Management and System from Peking University, an M.S. in Data Science, and a Ph.D. in Informatics from Indiana University Bloomington. His research applies multidisciplinary methods to scholarly big data. Yi aims to contextualize and elucidate the social dynamics of the global scientific ecosystem by leveraging massive datasets, computational techniques, and social theories. 
• Wei Lu is a professor of School of Information Management and director of Information Retrieval and Knowledge Mining Center, Wuhan University. He received his PhD degree of Information Science from Wuhan University, China. His current research interests include information retrieval, text mining, QA etc. He has papers published on SIGIR, Information Sciences, JASIST, IP&M. He serves as diverse roles (e.g., Associate Editor, Editorial Board Member, and Managing Guest Editor) for several journals.
• Jian Wu is an assistant professor of Computer Science at Old Dominion University. His research interests include natural language processing and understanding, scholarly big data, information retrieval, digital libraries, and science of science. He has published 70 peer-reviewed papers on ACM, IEEE, and AAAI venues, with best papers and nominations. He was rated the best reviewer in JCDL 2018. 

Venue

The conference will be held as part of the ACM/IEEE Joint Conference on Digital Libraries (JCDL) 2024 at University of Hong Kong, Hong Kong, Dec 19th, 2024.

Contact

All questions about submissions should be emailed to Dr. Haihua Chen (haihua.chen@unt.edu).