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  • × author_ss:"Zhang, Y."
  • × year_i:[2020 TO 2030}
  1. 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) 0.02 
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    Abstract
    Evaluating the quality of online health information (OHI) is a major challenge facing consumers. We designed PageGraph, an interface that displays quality indicators and associated values for a webpage, based on credibility evaluation models, the nudge theory, and existing empirical research concerning professionals' and consumers' evaluation of OHI quality. A qualitative evaluation of the interface with 16 participants revealed that PageGraph rendered the information and presentation nudges as intended. It provided the participants with easier access to quality indicators, encouraged fresh angles to assess information credibility, provided an evaluation framework, and encouraged validation of initial judgments. We then conducted a quantitative evaluation of the interface involving 60 participants using a between-subject experimental design. The control group used a regular web browser and evaluated the credibility of 12 preselected webpages, whereas the experimental group evaluated the same webpages with the assistance of PageGraph. PageGraph did not significantly influence participants' evaluation results. The results may be attributed to the insufficiency of the saliency and structure of the nudges implemented and the webpage stimuli's lack of sensitivity to the intervention. Future directions for applying nudges to support OHI evaluation were discussed.
    Date
    22. 6.2023 18:18:34
    Source
    Journal of the Association for Information Science and Technology. 74(2023) no.7, S.828-845
  2. 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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    Abstract
    Predicting a researcher's knowledge trajectories beyond their current foci can leverage potential inter-/cross-/multi-disciplinary interactions to achieve exploratory innovation. In this study, we present a method of diffusion-based network analytics for knowledge trajectory recommendation. The method begins by constructing a heterogeneous bibliometric network consisting of a co-topic layer and a co-authorship layer. A novel link prediction approach with a diffusion strategy is then used to capture the interactions between social elements (e.g., collaboration) and knowledge elements (e.g., technological similarity) in the process of exploratory innovation. This diffusion strategy differentiates the interactions occurring among homogeneous and heterogeneous nodes in the heterogeneous bibliometric network and weights the strengths of these interactions. Two sets of experiments-one with a local dataset and the other with a global dataset-demonstrate that the proposed method is prior to 10 selected baselines in link prediction, recommender systems, and upstream graph representation learning. A case study recommending knowledge trajectories of information scientists with topical hierarchy and explainable mediators reveals the proposed method's reliability and potential practical uses in broad scenarios.
    Date
    22. 6.2023 18:07:12
    Source
    Journal of the Association for Information Science and Technology. 74(2023) no.7, S.775-790
  3. Trace, C.B.; Zhang, Y.; Yi, S.; Williams-Brown, M.Y.: Information practices around genetic testing for ovarian cancer patients (2023) 0.01 
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    Abstract
    Knowledge of ovarian cancer patients' information practices around cancer genetic testing (GT) is needed to inform interventions that promote patient access to GT-related information. We interviewed 21 ovarian cancer patients and survivors who had GT as part of the treatment process and analyzed the transcripts using the qualitative content analysis method. We found that patients' information practices, manifested in their information-seeking mode, information sources utilized, information assessment, and information use, showed three distinct styles: passive, semi-active, and active. Patients with the passive style primarily received information from clinical sources, encountered information, or delegated information-seeking to family members; they were not inclined to assess information themselves and seldom used it to learn or influence others. Women with semi-active and active styles adopted more active information-seeking modes to approach information, utilized information sources beyond clinical settings, attempted to assess the information found, and actively used it to learn, educate others, or advocate GT to family and friends. Guided by the social ecological model, we found multiple levels of influences, including personal, interpersonal, organizational, community, and societal, acting as motivators or barriers to patients' information practice. Based on these findings, we discussed strategies to promote patient access to GT-related information.
    Source
    Journal of the Association for Information Science and Technology. 74(2023) no.11, S.1265-1281
  4. Xie, B.; He, D.; Mercer, T.; Wang, Y.; Wu, D.; Fleischmann, K.R.; Zhang, Y.; Yoder, L.H.; Stephens, K.K.; Mackert, M.; Lee, M.K.: Global health crises are also information crises : a call to action (2020) 0.01 
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    Abstract
    In this opinion paper, we argue that global health crises are also information crises. Using as an example the coronavirus disease 2019 (COVID-19) epidemic, we (a) examine challenges associated with what we term "global information crises"; (b) recommend changes needed for the field of information science to play a leading role in such crises; and (c) propose actionable items for short- and long-term research, education, and practice in information science.
    Source
    Journal of the Association for Information Science and Technology. 71(2020) no.12, S.1419-1423
  5. Zhang, M.; Zhang, Y.: Professional organizations in Twittersphere : an empirical study of U.S. library and information science professional organizations-related Tweets (2020) 0.01 
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    Abstract
    Twitter is utilized by many, including professional businesses and organizations; however, there are very few studies on how other entities interact with these organizations in the Twittersphere. This article presents a study that investigates tweets related to 5 major library and information science (LIS) professional organizations in the United States. This study applies a systematic tweets analysis framework, including descriptive analytics, network analytics, and co-word analysis of hashtags. The findings shed light on user engagement with LIS professional organizations and the trending discussion topics on Twitter, which is valuable for enabling more successful social media use and greater influence.
    Source
    Journal of the Association for Information Science and Technology. 71(2020) no.4, S.491-496
  6. Zhang, Y.; Zheng, G.; Yan, H.: Bridging information and communication technology and older adults by social network : an action research in Sichuan, China (2023) 0.00 
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    Abstract
    The extant literature demonstrates that the age-related digital divide prevents older adults from enhancing their quality of life. To bridge this gap and promote active aging, this study explores the interplay between social networks and older adults' use of information and communication technology (ICT). Using an action-oriented field research approach, we offered technical help (29 help sessions) to older adult participants recruited from western China. Then, we conducted content analysis to examine the obtained video, audio, and text data. Our results show that, first, different types of social networks significantly influence older adults' ICT use in terms of digital skills, engagement, and attitudes; however, these effects vary from person to person. In particular, our results highlight the crucial role of a stable and long-term supportive social network in learning and mastering ICT for older residents. Second, technical help facilitates the building and reinforcing of such a social network for the participants. Our study has strong implications in that policymakers can foster the digital inclusion of older people through supportive social networks.
    Content
    Beitrag in: JASIST special issue on ICT4D and intersections with the information field. Vgl.: https://asistdl.onlinelibrary.wiley.com/doi/10.1002/asi.24700.
    Source
    Journal of the Association for Information Science and Technology. 74(2023) no.12, S.1437-1448
  7. Zhang, Y.; Trace, C.B.: ¬The quality of health and wellness self-tracking data : a consumer perspective (2022) 0.00 
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    Abstract
    Information quality (IQ) is key to users' satisfaction with information systems. Understanding what IQ means to users can effectively inform system improvement. Existing inquiries into self-tracking data quality primarily focus on accuracy. Interviewing 20 consumers who had self-tracked health indicators for at least 6 months, we identified eight dimensions that consumers apply to evaluate self-tracking data quality: value-added, accuracy, completeness, accessibility, ease of understanding, trustworthiness, aesthetics, and invasiveness. These dimensions fell into four categories-intrinsic, contextual, representational, and accessibility-suggesting that consumers judge self-tracking data quality not only based on the data's inherent quality but also considering tasks at hand, the clarity of data representation, and data accessibility. We also found that consumers' self-tracking data quality judgments are shaped primarily by their goals or motivations, subjective experience with tracked activities, mental models of how systems work, self-tracking tools' reputation, cost, and design, and domain knowledge and intuition, but less by more objective criteria such as scientific research results, validated devices, or consultation with experts. Future studies should develop and validate a scale for measuring consumers' perceptions of self-tracking data quality and commit efforts to develop technologies and training materials to enhance consumers' ability to evaluate data quality.
    Source
    Journal of the Association for Information Science and Technology. 73(2022) no.6, S.879-891
  8. Zhang, Y.; Ren, P.; Rijke, M. de: ¬A taxonomy, data set, and benchmark for detecting and classifying malevolent dialogue responses (2021) 0.00 
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    Abstract
    Conversational interfaces are increasingly popular as a way of connecting people to information. With the increased generative capacity of corpus-based conversational agents comes the need to classify and filter out malevolent responses that are inappropriate in terms of content and dialogue acts. Previous studies on the topic of detecting and classifying inappropriate content are mostly focused on a specific category of malevolence or on single sentences instead of an entire dialogue. We make three contributions to advance research on the malevolent dialogue response detection and classification (MDRDC) task. First, we define the task and present a hierarchical malevolent dialogue taxonomy. Second, we create a labeled multiturn dialogue data set and formulate the MDRDC task as a hierarchical classification task. Last, we apply state-of-the-art text classification methods to the MDRDC task, and report on experiments aimed at assessing the performance of these approaches.
    Source
    Journal of the Association for Information Science and Technology. 72(2021) no.12, S.1477-1497
  9. Shah, C.; Anderson, T.; Hagen, L.; Zhang, Y.: ¬An iSchool approach to data science : human-centered, socially responsible, and context-driven (2021) 0.00 
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    Abstract
    The Information Schools, also referred to as iSchools, have a unique approach to data science with three distinct components: human-centeredness, socially responsible, and rooted in context. In this position paper, we highlight and expand on these components and show how they are integrated in various research and educational activities related to data science that are being carried out at iSchools. We argue that the iSchool way of doing data science is not only highly relevant to the current times, but also crucial in solving problems of tomorrow. Specifically, we accentuate the issues of developing insights and solutions that are not only data-driven, but also incorporate human values, including transparency, privacy, ethics, fairness, and equity. This approach to data science has meaningful implications on how we educate the students and train the next generation of scholars and policymakers. Here, we provide some of those design decisions, rooted in evidence-based research, along with our perspective on how data science is currently situated and how it should be advanced in iSchools.
    Source
    Journal of the Association for Information Science and Technology. 72(2021) no.6, S.793-796
  10. Xu, H.; Bu, Y.; Liu, M.; Zhang, C.; Sun, M.; Zhang, Y.; Meyer, E.; Salas, E.; Ding, Y.: Team power dynamics and team impact : new perspectives on scientific collaboration using career age as a proxy for team power (2022) 0.00 
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    Abstract
    Power dynamics influence every aspect of scientific collaboration. Team power dynamics can be measured by team power level and team power hierarchy. Team power level is conceptualized as the average level of the possession of resources, expertise, or decision-making authorities of a team. Team power hierarchy represents the vertical differences of the possessions of resources in a team. In Science of Science, few studies have looked at scientific collaboration from the perspective of team power dynamics. This research examines how team power dynamics affect team impact to fill the research gap. In this research, all coauthors of one publication are treated as one team. Team power level and team power hierarchy of one team are measured by the mean and Gini index of career age of coauthors in this team. Team impact is quantified by citations of a paper authored by this team. By analyzing over 7.7 million teams from Science (e.g., Computer Science, Physics), Social Sciences (e.g., Sociology, Library & Information Science), and Arts & Humanities (e.g., Art), we find that flat team structure is associated with higher team impact, especially when teams have high team power level. These findings have been repeated in all five disciplines except Art, and are consistent in various types of teams from Computer Science including teams from industry or academia, teams with different gender groups, teams with geographical contrast, and teams with distinct size.
    Source
    Journal of the Association for Information Science and Technology. 73(2022) no.10, S.1489-1505
  11. Zhang, Y.; Wu, D.; Hagen, L.; Song, I.-Y.; Mostafa, J.; Oh, S.; Anderson, T.; Shah, C.; Bishop, B.W.; Hopfgartner, F.; Eckert, K.; Federer, L.; Saltz, J.S.: Data science curriculum in the iField (2023) 0.00 
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    Abstract
    Many disciplines, including the broad Field of Information (iField), offer Data Science (DS) programs. There have been significant efforts exploring an individual discipline's identity and unique contributions to the broader DS education landscape. To advance DS education in the iField, the iSchool Data Science Curriculum Committee (iDSCC) was formed and charged with building and recommending a DS education framework for iSchools. This paper reports on the research process and findings of a series of studies to address important questions: What is the iField identity in the multidisciplinary DS education landscape? What is the status of DS education in iField schools? What knowledge and skills should be included in the core curriculum for iField DS education? What are the jobs available for DS graduates from the iField? What are the differences between graduate-level and undergraduate-level DS education? Answers to these questions will not only distinguish an iField approach to DS education but also define critical components of DS curriculum. The results will inform individual DS programs in the iField to develop curriculum to support undergraduate and graduate DS education in their local context.
    Source
    Journal of the Association for Information Science and Technology. 74(2023) no.6, S.641-662
  12. Zhang, Y.; Li, X.; Fan, W.: User adoption of physician's replies in an online health community : an empirical study (2020) 0.00 
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    Source
    Journal of the Association for Information Science and Technology. 71(2020) no.10, S.1179-1191
  13. Zhang, Y.; Zhang, C.; Li, J.: Joint modeling of characters, words, and conversation contexts for microblog keyphrase extraction (2020) 0.00 
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    Source
    Journal of the Association for Information Science and Technology. 71(2020) no.5, S.553-567
  14. 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) 0.00 
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    Source
    Journal of the Association for Information Science and Technology. 71(2020) no.10, S.1162-1178
  15. Zhang, Y.; Zhang, C.: Enhancing keyphrase extraction from microblogs using human reading time (2021) 0.00 
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    Source
    Journal of the Association for Information Science and Technology. 72(2021) no.5, S.611-626

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