Hsinchun Chen

Hsinchun Chen

Professor, Management Information Systems
Regents Professor
Member of the Graduate Faculty
Professor, BIO5 Institute
Primary Department
Contact
(520) 621-4153

Research Interest

Dr Chen's areas of expertise include:Security informatics, security big data; smart and connected health, health analytics; data, text, web mining.Digital library, intelligent information retrieval, automatic categorization and classification, machine learning for IR, large-scale information analysis and visualization.Internet resource discovery, digital libraries, IR for large-scale scientific and business databases, customized IR, multilingual IR.Knowledge-based systems design, knowledge discovery in databases, hypertext systems, machine learning, neural networks computing, genetic algorithms, simulated annealing.Cognitive modeling, human-computer interactions, IR behaviors, human problem-solving process.

Publications

Jiang, S., & Chen, H. (2016). NATERGM: A Model for Examining the Role of Nodal Attributes in Dynamic Social Media Networks. IEEE Transactions on Knowledge and Data Engineering, Forthcoming: 28(3), 729-740.
Wu, B., Jiang, S., & Chen, H. (2015). The impact of individual attributes on knowledge diffusion in web forums. QUALITY & QUANTITY, 49(6), 2221-2236.
Schatz, B., & Chen, H. (1999). Digital Libraries: Technological Advances and Social Impacts. Computer, 32(2), 45-X.

Abstract:

Public awareness of the Net as a critical infrastructure in the 1990s has spurred a new revolution in the technologies for information retrieval in digital libraries.

Abbasi, A., & Chen, H. (2007). Categorization and analysis of text in computer mediated communication archives using visualization. Proceedings of the ACM International Conference on Digital Libraries, 11-18.

Abstract:

Digital libraries (DLs) for online discourse contain large amounts of valuable information that is difficult to navigate and analyze. Visualization systems developed to facilitate improved CMC archive analysis and navigation primarily focus on interaction information, with little emphasis on textual content. In this paper we present a system that provides DL exploration services such as visualization, categorization, and analysis for CMC text. The system incorporates an extended feature set comprised of stylistic, topical, and sentiment related features to enable richer content representation. The system also includes the Ink Blot technique which utilizes decision tree models and text overlay to visualize CMC messages. Ink Blots can be used for text categorization and analysis across forums, authors, threads, messages, and over time. The proposed system's analysis capabilities were evaluated with a series of examples and a qualitative user study. Empirical categorization experiments comparing the Ink Blot technique against a benchmark support vector machine classifier were also conducted. The results demonstrated the efficacy of the Ink Blot technique for text categorization and also highlighted the effectiveness of the extended feature set for improved text categorization. Copyright 2007 ACM.

Lin, C., Hu, P. J., & Chen, H. (2004). Technology Implementation Management in Law Enforcement: COPLINK System Usability and User Acceptance Evaluations. Social Science Computer Review, 22(1), 24-36.

Abstract:

Increasingly, government agencies are facing the challenge of effective implementation of information technologies critical to their digital government programs and initiatives. This article reports two user-centric evaluation studies of COPLINK, an integrated knowledge management system that supports and enhances law enforcement officers' crime-fighting activities. Specifically, the evaluations concentrate on system usability and user acceptance in the law enforcement setting. The article describes the study designs, highlights the analysis results, and discusses their implications for digital government research and practices. Findings from these studies provide valuable insights into digital government system evaluation and, at the same time, shed light on how government agencies can design adequate management interventions to foster technology acceptance and use.