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  • × author_ss:"Zhang, X."
  • × year_i:[2020 TO 2030}
  1. Yang, F.; Zhang, X.: Focal fields in literature on the information divide : the USA, China, UK and India (2020) 0.00 
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    Abstract
    Purpose The purpose of this paper is to identify key countries and their focal research fields on the information divide. Design/methodology/approach Literature was retrieved to identify key countries and their primary focus. The literature research method was adopted to identify aspects of the primary focus in each key country. Findings The key countries with literature on the information divide are the USA, China, the UK and India. The problem of health is prominent in the USA, and solutions include providing information, distinguishing users' profiles and improving eHealth literacy. Economic and political factors led to the urban-rural information divide in China, and policy is the most powerful solution. Under the influence of humanism, research on the information divide in the UK focuses on all age groups, and solutions differ according to age. Deep-rooted patriarchal concepts and traditional marriage customs make the gender information divide prominent in India, and increasing women's information consciousness is a feasible way to reduce this divide. Originality/value This paper is an extensive review study on the information divide, which clarifies the key countries and their focal fields in research on this topic. More important, the paper innovatively analyzes and summarizes existing literature from a country perspective.
    Date
    13. 2.2020 18:22:13
    Theme
    Information
  2. Zhang, X.; Wang, D.; Tang, Y.; Xiao, Q.: How question type influences knowledge withholding in social Q&A community (2023) 0.00 
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    Date
    22. 9.2023 13:51:47
    Source
    Journal of the Association for Information Science and Technology. 74(2023) no.10, S.1170-1184
  3. Tay, W.; Zhang, X.; Karimi , S.: Beyond mean rating : probabilistic aggregation of star ratings based on helpfulness (2020) 0.00 
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    Abstract
    The star-rating mechanism of customer reviews is used universally by the online population to compare and select merchants, movies, products, and services. The consensus opinion from aggregation of star ratings is used as a proxy for item quality. Online reviews are noisy and effective aggregation of star ratings to accurately reflect the "true quality" of products and services is challenging. The mean-rating aggregation model is widely used and other aggregation models are also proposed. These existing aggregation models rely on a large number of reviews to tolerate noise. However, many products rarely have reviews. We propose probabilistic aggregation models for review ratings based on the Dirichlet distribution to combat data sparsity in reviews. We further propose to exploit the "helpfulness" social information and time to filter noisy reviews and effectively aggregate ratings to compute the consensus opinion. Our experiments on an Amazon data set show that our probabilistic aggregation models based on "helpfulness" achieve better performance than the statistical and heuristic baseline approaches.
    Source
    Journal of the Association for Information Science and Technology. 71(2020) no.7, S.784-799
  4. Cui, Y.; Wang, Y.; Liu, X.; Wang, X.; Zhang, X.: Multidimensional scholarly citations : characterizing and understanding scholars' citation behaviors (2023) 0.00 
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    Source
    Journal of the Association for Information Science and Technology. 74(2023) no.1, S.115-127

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