Search (12 results, page 1 of 1)

Zhang, Y.: ¬The impact of Internet-based electronic resources on formal scholarly communication in the area of library and information science : a citation analysis (1998) 0.01

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Date: 30. 1.1999 17:22:22

Zhang, Y.: Developing a holistic model for digital library evaluation (2010) 0.01
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Abstract

This article reports the author's recent research in developing a holistic model for various levels of digital library (DL) evaluation in which perceived important criteria from heterogeneous stakeholder groups are organized and presented. To develop such a model, the author applied a three-stage research approach: exploration, confirmation, and verification. During the exploration stage, a literature review was conducted followed by an interview, along with a card sorting technique, to collect important criteria perceived by DL experts. Then the criteria identified were used for developing an online survey during the confirmation stage. Survey respondents (431 in total) from 22 countries rated the importance of the criteria. A holistic DL evaluation model was constructed using statistical techniques. Eventually, the verification stage was devised to test the reliability of the model in the context of searching and evaluating an operational DL. The proposed model fills two lacunae in the DL domain: (a) the lack of a comprehensive and flexible framework to guide and benchmark evaluations, and (b) the uncertainty about what divergence exists among heterogeneous DL stakeholders, including general users.

Zhang, Y.; Jansen, B.J.; Spink, A.: Identification of factors predicting clickthrough in Web searching using neural network analysis (2009) 0.01

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Date: 22. 3.2009 17:49:11

Zhang, Y.; Liu, J.; Song, S.: ¬The design and evaluation of a nudge-based interface to facilitate consumers' evaluation of online health information credibility (2023) 0.01

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Date: 22. 6.2023 18:18:34

Zhang, Y.; Wu, M.; Zhang, G.; Lu, J.: Stepping beyond your comfort zone : diffusion-based network analytics for knowledge trajectory recommendation (2023) 0.01

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Date: 22. 6.2023 18:07:12

Tenopir, C.; Wang, P.; Zhang, Y.; Simmons, B.; Pollard, R.: Academic users' interactions with ScienceDirect in search tasks : affective and cognitive behaviors (2008) 0.01
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Abstract

This article presents part of phase 2 of a research project funded by the NSF-National Science Digital Library Project, which observed how academic users interact with the ScienceDirect information retrieval system for simulated class-related assignments. The ultimate goal of the project is twofold: (1) to find ways to improve science and engineering students' use of science e-journal systems; (2) to develop methods to measure user interaction behaviors. Process-tracing technique recorded participants' processes and interaction behaviors that are measurable; think-aloud protocol captured participants' affective and cognitive verbalizations; pre- and post-search questionnaires solicited demographic information, prior experience with the system, and comments. We explored possible relationships between affective feelings and cognitive behaviors. During search interactions both feelings and thoughts occurred frequently. Positive feelings were more common and were associated more often with thoughts about results. Negative feelings were associated more often with thoughts related to the system, search strategy, and task. Learning styles are also examined as a factor influencing behavior. Engineering graduate students with an assimilating learning style searched longer and paused less than those with a converging learning style. Further exploration of learning styles is suggested.
Zhang, Y.; Zhang, C.; Li, J.: Joint modeling of characters, words, and conversation contexts for microblog keyphrase extraction (2020) 0.01
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Abstract

Millions of messages are produced on microblog platforms every day, leading to the pressing need for automatic identification of key points from the massive texts. To absorb salient content from the vast bulk of microblog posts, this article focuses on the task of microblog keyphrase extraction. In previous work, most efforts treat messages as independent documents and might suffer from the data sparsity problem exhibited in short and informal microblog posts. On the contrary, we propose to enrich contexts via exploiting conversations initialized by target posts and formed by their replies, which are generally centered around relevant topics to the target posts and therefore helpful for keyphrase identification. Concretely, we present a neural keyphrase extraction framework, which has 2 modules: a conversation context encoder and a keyphrase tagger. The conversation context encoder captures indicative representation from their conversation contexts and feeds the representation into the keyphrase tagger, and the keyphrase tagger extracts salient words from target posts. The 2 modules were trained jointly to optimize the conversation context encoding and keyphrase extraction processes. In the conversation context encoder, we leverage hierarchical structures to capture the word-level indicative representation and message-level indicative representation hierarchically. In both of the modules, we apply character-level representations, which enables the model to explore morphological features and deal with the out-of-vocabulary problem caused by the informal language style of microblog messages. Extensive comparison results on real-life data sets indicate that our model outperforms state-of-the-art models from previous studies.
Lu, C.; Zhang, Y.; Ahn, Y.-Y.; Ding, Y.; Zhang, C.; Ma, D.: Co-contributorship network and division of labor in individual scientific collaborations (2020) 0.01
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Abstract

Collaborations are pervasive in current science. Collaborations have been studied and encouraged in many disciplines. However, little is known about how a team really functions from the detailed division of labor within. In this research, we investigate the patterns of scientific collaboration and division of labor within individual scholarly articles by analyzing their co-contributorship networks. Co-contributorship networks are constructed by performing the one-mode projection of the author-task bipartite networks obtained from 138,787 articles published in PLoS journals. Given an article, we define 3 types of contributors: Specialists, Team-players, and Versatiles. Specialists are those who contribute to all their tasks alone; team-players are those who contribute to every task with other collaborators; and versatiles are those who do both. We find that team-players are the majority and they tend to contribute to the 5 most common tasks as expected, such as "data analysis" and "performing experiments." The specialists and versatiles are more prevalent than expected by our designed 2 null models. Versatiles tend to be senior authors associated with funding and supervision. Specialists are associated with 2 contrasting roles: the supervising role as team leaders or marginal and specialized contributors.

Zhang, Y.; Salaba, A.: What do users tell us about FRBR-based catalogs? (2012) 0.01

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Abstract: FRBR user research has been the least addressed area in FRBR research and development. This article addresses the research gap in evaluating and designing catalogs based on FRBR user research. It draws from three user studies concerning FRBR-based catalogs: (1) user evaluation of three FRBR-based catalogs, (2) user participatory design of a prototype catalog based on the FRBR model, and (3) user evaluation of the resulting FRBR prototype catalog. The major findings from the user studies are highlighted and discussed for future development of FRBR-based catalogs that support various user tasks.

Ku, Y.; Chiu, C.; Zhang, Y.; Chen, H.; Su, H.: Text mining self-disclosing health information for public health service (2014) 0.01
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Abstract

Understanding specific patterns or knowledge of self-disclosing health information could support public health surveillance and healthcare. This study aimed to develop an analytical framework to identify self-disclosing health information with unusual messages on web forums by leveraging advanced text-mining techniques. To demonstrate the performance of the proposed analytical framework, we conducted an experimental study on 2 major human immunodeficiency virus (HIV)/acquired immune deficiency syndrome (AIDS) forums in Taiwan. The experimental results show that the classification accuracy increased significantly (up to 83.83%) when using features selected by the information gain technique. The results also show the importance of adopting domain-specific features in analyzing unusual messages on web forums. This study has practical implications for the prevention and support of HIV/AIDS healthcare. For example, public health agencies can re-allocate resources and deliver services to people who need help via social media sites. In addition, individuals can also join a social media site to get better suggestions and support from each other.
Zhang, X.; Fang, Y.; He, W.; Zhang, Y.; Liu, X.: Epistemic motivation, task reflexivity, and knowledge contribution behavior on team wikis : a cross-level moderation model (2019) 0.01
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Abstract

A cross-level model based on the information processing perspective and trait activation theory was developed and tested in order to investigate the effects of individual-level epistemic motivation and team-level task reflexivity on three different individual contribution behaviors (i.e., adding, deleting, and revising) in the process of knowledge creation on team wikis. Using the Hierarchical Linear Modeling software package and the 2-wave data from 166 individuals in 51 wiki-based teams, we found cross-level interaction effects between individual epistemic motivation and team task reflexivity on different knowledge contribution behaviors on wikis. Epistemic motivation exerted a positive effect on adding, which was strengthened by team task reflexivity. The effect of epistemic motivation on deleting was positive only when task reflexivity was high. In addition, epistemic motivation was strongly positively related to revising, regardless of the level of task reflexivity involved.
Zhang, Y.: Beyond quality and accessibility : source selection in consumer health information searching (2014) 0.00
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Abstract

A systematic understanding of factors and criteria that affect consumers' selection of sources for health information is necessary for the design of effective health information services and information systems. However, current studies have overly focused on source attributes as indicators for 2 criteria, source quality and accessibility, and overlooked the role of other factors and criteria that help determine source selection. To fill this gap, guided by decision-making theories and the cognitive perspective to information search, we interviewed 30 participants about their reasons for using a wide range of sources for health information. Additionally, we asked each of them to report a critical incident in which sources were selected to fulfill a specific information need. Based on the analysis of the transcripts, 5 categories of factors were identified as influential to source selection: source-related factors, user-related factors, user-source relationships, characteristics of the problematic situation, and social influences. In addition, about a dozen criteria that mediate the influence of the factors on source-selection decisions were identified, including accessibility, quality, usability, interactivity, relevance, usefulness, familiarity, affection, anonymity, and appropriateness. These results significantly expanded the current understanding of the nature of costs and benefits involved in source-selection decisions, and strongly indicated that a personalized approach is needed for information services and information systems to provide effective access to health information sources for consumers.

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