Search (43 results, page 1 of 3)

  • × author_ss:"Zhang, Y."
  1. Zhang, Y.; Jansen, B.J.; Spink, A.: Identification of factors predicting clickthrough in Web searching using neural network analysis (2009) 0.06
    0.060201935 = product of:
      0.096323095 = sum of:
        0.025048172 = weight(_text_:retrieval in 2742) [ClassicSimilarity], result of:
          0.025048172 = score(doc=2742,freq=2.0), product of:
            0.124912694 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.041294612 = queryNorm
            0.20052543 = fieldWeight in 2742, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.046875 = fieldNorm(doc=2742)
        0.025667597 = weight(_text_:use in 2742) [ClassicSimilarity], result of:
          0.025667597 = score(doc=2742,freq=2.0), product of:
            0.12644777 = queryWeight, product of:
              3.0620887 = idf(docFreq=5623, maxDocs=44218)
              0.041294612 = queryNorm
            0.20298971 = fieldWeight in 2742, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.0620887 = idf(docFreq=5623, maxDocs=44218)
              0.046875 = fieldNorm(doc=2742)
        0.022201622 = weight(_text_:of in 2742) [ClassicSimilarity], result of:
          0.022201622 = score(doc=2742,freq=22.0), product of:
            0.06457475 = queryWeight, product of:
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.041294612 = queryNorm
            0.34381276 = fieldWeight in 2742, product of:
              4.690416 = tf(freq=22.0), with freq of:
                22.0 = termFreq=22.0
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.046875 = fieldNorm(doc=2742)
        0.006621159 = product of:
          0.013242318 = sum of:
            0.013242318 = weight(_text_:on in 2742) [ClassicSimilarity], result of:
              0.013242318 = score(doc=2742,freq=2.0), product of:
                0.090823986 = queryWeight, product of:
                  2.199415 = idf(docFreq=13325, maxDocs=44218)
                  0.041294612 = queryNorm
                0.14580199 = fieldWeight in 2742, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  2.199415 = idf(docFreq=13325, maxDocs=44218)
                  0.046875 = fieldNorm(doc=2742)
          0.5 = coord(1/2)
        0.016784549 = product of:
          0.033569098 = sum of:
            0.033569098 = weight(_text_:22 in 2742) [ClassicSimilarity], result of:
              0.033569098 = score(doc=2742,freq=2.0), product of:
                0.1446067 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.041294612 = queryNorm
                0.23214069 = fieldWeight in 2742, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.046875 = fieldNorm(doc=2742)
          0.5 = coord(1/2)
      0.625 = coord(5/8)
    
    Abstract
    In this research, we aim to identify factors that significantly affect the clickthrough of Web searchers. Our underlying goal is determine more efficient methods to optimize the clickthrough rate. We devise a clickthrough metric for measuring customer satisfaction of search engine results using the number of links visited, number of queries a user submits, and rank of clicked links. We use a neural network to detect the significant influence of searching characteristics on future user clickthrough. Our results show that high occurrences of query reformulation, lengthy searching duration, longer query length, and the higher ranking of prior clicked links correlate positively with future clickthrough. We provide recommendations for leveraging these findings for improving the performance of search engine retrieval and result ranking, along with implications for search engine marketing.
    Date
    22. 3.2009 17:49:11
    Source
    Journal of the American Society for Information Science and Technology. 60(2009) no.3, S.557-570
  2. Tenopir, C.; Wang, P.; Zhang, Y.; Simmons, B.; Pollard, R.: Academic users' interactions with ScienceDirect in search tasks : affective and cognitive behaviors (2008) 0.02
    0.024215013 = product of:
      0.06457337 = sum of:
        0.029519552 = weight(_text_:retrieval in 2027) [ClassicSimilarity], result of:
          0.029519552 = score(doc=2027,freq=4.0), product of:
            0.124912694 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.041294612 = queryNorm
            0.23632148 = fieldWeight in 2027, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2027)
        0.021389665 = weight(_text_:use in 2027) [ClassicSimilarity], result of:
          0.021389665 = score(doc=2027,freq=2.0), product of:
            0.12644777 = queryWeight, product of:
              3.0620887 = idf(docFreq=5623, maxDocs=44218)
              0.041294612 = queryNorm
            0.1691581 = fieldWeight in 2027, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.0620887 = idf(docFreq=5623, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2027)
        0.013664153 = weight(_text_:of in 2027) [ClassicSimilarity], result of:
          0.013664153 = score(doc=2027,freq=12.0), product of:
            0.06457475 = queryWeight, product of:
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.041294612 = queryNorm
            0.21160212 = fieldWeight in 2027, product of:
              3.4641016 = tf(freq=12.0), with freq of:
                12.0 = termFreq=12.0
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2027)
      0.375 = coord(3/8)
    
    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.
    Footnote
    Beitrag eines Themenbereichs: Evaluation of Interactive Information Retrieval Systems
  3. Zhang, Y.; Xu, W.: Fast exact maximum likelihood estimation for mixture of language model (2008) 0.02
    0.02329753 = product of:
      0.062126745 = sum of:
        0.035423465 = weight(_text_:retrieval in 2082) [ClassicSimilarity], result of:
          0.035423465 = score(doc=2082,freq=4.0), product of:
            0.124912694 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.041294612 = queryNorm
            0.2835858 = fieldWeight in 2082, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.046875 = fieldNorm(doc=2082)
        0.02008212 = weight(_text_:of in 2082) [ClassicSimilarity], result of:
          0.02008212 = score(doc=2082,freq=18.0), product of:
            0.06457475 = queryWeight, product of:
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.041294612 = queryNorm
            0.3109903 = fieldWeight in 2082, product of:
              4.2426405 = tf(freq=18.0), with freq of:
                18.0 = termFreq=18.0
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.046875 = fieldNorm(doc=2082)
        0.006621159 = product of:
          0.013242318 = sum of:
            0.013242318 = weight(_text_:on in 2082) [ClassicSimilarity], result of:
              0.013242318 = score(doc=2082,freq=2.0), product of:
                0.090823986 = queryWeight, product of:
                  2.199415 = idf(docFreq=13325, maxDocs=44218)
                  0.041294612 = queryNorm
                0.14580199 = fieldWeight in 2082, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  2.199415 = idf(docFreq=13325, maxDocs=44218)
                  0.046875 = fieldNorm(doc=2082)
          0.5 = coord(1/2)
      0.375 = coord(3/8)
    
    Abstract
    Language modeling is an effective and theoretically attractive probabilistic framework for text information retrieval. The basic idea of this approach is to estimate a language model of a given document (or document set), and then do retrieval or classification based on this model. A common language modeling approach assumes the data D is generated from a mixture of several language models. The core problem is to find the maximum likelihood estimation of one language model mixture, given the fixed mixture weights and the other language model mixture. The EM algorithm is usually used to find the solution. In this paper, we proof that an exact maximum likelihood estimation of the unknown mixture component exists and can be calculated using the new algorithm we proposed. We further improve the algorithm and provide an efficient algorithm of O(k) complexity to find the exact solution, where k is the number of words occurring at least once in data D. Furthermore, we proof the probabilities of many words are exactly zeros, and the MLE estimation is implemented as a feature selection technique explicitly.
  4. Zhang, Y.: Undergraduate students' mental models of the Web as an information retrieval system (2008) 0.02
    0.022097845 = product of:
      0.05892759 = sum of:
        0.029519552 = weight(_text_:retrieval in 2385) [ClassicSimilarity], result of:
          0.029519552 = score(doc=2385,freq=4.0), product of:
            0.124912694 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.041294612 = queryNorm
            0.23632148 = fieldWeight in 2385, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2385)
        0.021604925 = weight(_text_:of in 2385) [ClassicSimilarity], result of:
          0.021604925 = score(doc=2385,freq=30.0), product of:
            0.06457475 = queryWeight, product of:
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.041294612 = queryNorm
            0.33457235 = fieldWeight in 2385, product of:
              5.477226 = tf(freq=30.0), with freq of:
                30.0 = termFreq=30.0
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2385)
        0.007803111 = product of:
          0.015606222 = sum of:
            0.015606222 = weight(_text_:on in 2385) [ClassicSimilarity], result of:
              0.015606222 = score(doc=2385,freq=4.0), product of:
                0.090823986 = queryWeight, product of:
                  2.199415 = idf(docFreq=13325, maxDocs=44218)
                  0.041294612 = queryNorm
                0.1718293 = fieldWeight in 2385, product of:
                  2.0 = tf(freq=4.0), with freq of:
                    4.0 = termFreq=4.0
                  2.199415 = idf(docFreq=13325, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=2385)
          0.5 = coord(1/2)
      0.375 = coord(3/8)
    
    Abstract
    This study explored undergraduate students' mental models of the Web as an information retrieval system. Mental models play an important role in people's interaction with information systems. Better understanding of people's mental models could inspire better interface design and user instruction. Multiple data-collection methods, including questionnaire, semistructured interview, drawing, and participant observation, were used to elicit students' mental models of the Web from different perspectives, though only data from interviews and drawing descriptions are reported in this article. Content analysis of the transcripts showed that students had utilitarian rather than structural mental models of the Web. The majority of participants saw the Web as a huge information resource where everything can be found rather than an infrastructure consisting of hardware and computer applications. Students had different mental models of how information is organized on the Web, and the models varied in correctness and complexity. Students' mental models of search on the Web were illustrated from three points of view: avenues of getting information, understanding of search engines' working mechanisms, and search tactics. The research results suggest that there are mainly three sources contributing to the construction of mental models: personal observation, communication with others, and class instruction. In addition to structural and functional aspects, mental models have an emotional dimension.
    Source
    Journal of the American Society for Information Science and Technology. 59(2008) no.13, S.2087-2098
  5. Zhang, M.; Zhang, Y.: Professional organizations in Twittersphere : an empirical study of U.S. library and information science professional organizations-related Tweets (2020) 0.02
    0.021319462 = product of:
      0.056851897 = sum of:
        0.029945528 = weight(_text_:use in 5775) [ClassicSimilarity], result of:
          0.029945528 = score(doc=5775,freq=2.0), product of:
            0.12644777 = queryWeight, product of:
              3.0620887 = idf(docFreq=5623, maxDocs=44218)
              0.041294612 = queryNorm
            0.23682132 = fieldWeight in 5775, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.0620887 = idf(docFreq=5623, maxDocs=44218)
              0.0546875 = fieldNorm(doc=5775)
        0.013526822 = weight(_text_:of in 5775) [ClassicSimilarity], result of:
          0.013526822 = score(doc=5775,freq=6.0), product of:
            0.06457475 = queryWeight, product of:
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.041294612 = queryNorm
            0.20947541 = fieldWeight in 5775, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.0546875 = fieldNorm(doc=5775)
        0.013379549 = product of:
          0.026759097 = sum of:
            0.026759097 = weight(_text_:on in 5775) [ClassicSimilarity], result of:
              0.026759097 = score(doc=5775,freq=6.0), product of:
                0.090823986 = queryWeight, product of:
                  2.199415 = idf(docFreq=13325, maxDocs=44218)
                  0.041294612 = queryNorm
                0.29462588 = fieldWeight in 5775, product of:
                  2.4494898 = tf(freq=6.0), with freq of:
                    6.0 = termFreq=6.0
                  2.199415 = idf(docFreq=13325, maxDocs=44218)
                  0.0546875 = fieldNorm(doc=5775)
          0.5 = coord(1/2)
      0.375 = coord(3/8)
    
    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.: Understanding the sustained use of online health communities from a self-determination perspective (2016) 0.02
    0.020145882 = product of:
      0.05372235 = sum of:
        0.037047986 = weight(_text_:use in 3216) [ClassicSimilarity], result of:
          0.037047986 = score(doc=3216,freq=6.0), product of:
            0.12644777 = queryWeight, product of:
              3.0620887 = idf(docFreq=5623, maxDocs=44218)
              0.041294612 = queryNorm
            0.29299045 = fieldWeight in 3216, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              3.0620887 = idf(docFreq=5623, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3216)
        0.011156735 = weight(_text_:of in 3216) [ClassicSimilarity], result of:
          0.011156735 = score(doc=3216,freq=8.0), product of:
            0.06457475 = queryWeight, product of:
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.041294612 = queryNorm
            0.17277241 = fieldWeight in 3216, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3216)
        0.0055176322 = product of:
          0.0110352645 = sum of:
            0.0110352645 = weight(_text_:on in 3216) [ClassicSimilarity], result of:
              0.0110352645 = score(doc=3216,freq=2.0), product of:
                0.090823986 = queryWeight, product of:
                  2.199415 = idf(docFreq=13325, maxDocs=44218)
                  0.041294612 = queryNorm
                0.121501654 = fieldWeight in 3216, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  2.199415 = idf(docFreq=13325, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=3216)
          0.5 = coord(1/2)
      0.375 = coord(3/8)
    
    Abstract
    Sustained use of an information source is sometimes important for achieving an individual's long-term goals, such as learning and self-development. It is even more important for users of online health communities because health benefits usually come with sustained use. However, little is known about what retains a user. We interviewed 21 participants who had been using online diabetes communities in a sustained manner. Guided by self-determination theory, which posits that behaviors are sustained when they can satisfy basic human needs for autonomy, competence, and relatedness, we identified mechanisms that help satisfy these needs, and thus sustain users in online health communities. Autonomy-supportive mechanisms include being respected and supported as a unique individual, feeling free in making choices, and receiving meaningful rationales about others' decisions. Competence-cultivating mechanisms include seeking information, providing information, and exchanging information with others to construct knowledge. Mechanisms that cultivate relatedness include seeing similarities between oneself and peers, receiving responses from others, providing emotional support, and forming small underground groups for closer interactions. The results suggest that, like emotions, information and small group interactions also play a key role in retaining users. System design and community management strategies are discussed based on these mechanisms.
    Source
    Journal of the Association for Information Science and Technology. 67(2016) no.12, S.22842-2857
  7. Zhang, Y.; Zhang, C.: Enhancing keyphrase extraction from microblogs using human reading time (2021) 0.02
    0.019665582 = product of:
      0.052441552 = sum of:
        0.030249555 = weight(_text_:use in 237) [ClassicSimilarity], result of:
          0.030249555 = score(doc=237,freq=4.0), product of:
            0.12644777 = queryWeight, product of:
              3.0620887 = idf(docFreq=5623, maxDocs=44218)
              0.041294612 = queryNorm
            0.23922569 = fieldWeight in 237, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.0620887 = idf(docFreq=5623, maxDocs=44218)
              0.0390625 = fieldNorm(doc=237)
        0.011156735 = weight(_text_:of in 237) [ClassicSimilarity], result of:
          0.011156735 = score(doc=237,freq=8.0), product of:
            0.06457475 = queryWeight, product of:
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.041294612 = queryNorm
            0.17277241 = fieldWeight in 237, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.0390625 = fieldNorm(doc=237)
        0.0110352645 = product of:
          0.022070529 = sum of:
            0.022070529 = weight(_text_:on in 237) [ClassicSimilarity], result of:
              0.022070529 = score(doc=237,freq=8.0), product of:
                0.090823986 = queryWeight, product of:
                  2.199415 = idf(docFreq=13325, maxDocs=44218)
                  0.041294612 = queryNorm
                0.24300331 = fieldWeight in 237, product of:
                  2.828427 = tf(freq=8.0), with freq of:
                    8.0 = termFreq=8.0
                  2.199415 = idf(docFreq=13325, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=237)
          0.5 = coord(1/2)
      0.375 = coord(3/8)
    
    Abstract
    The premise of manual keyphrase annotation is to read the corresponding content of an annotated object. Intuitively, when we read, more important words will occupy a longer reading time. Hence, by leveraging human reading time, we can find the salient words in the corresponding content. However, previous studies on keyphrase extraction ignore human reading features. In this article, we aim to leverage human reading time to extract keyphrases from microblog posts. There are two main tasks in this study. One is to determine how to measure the time spent by a human on reading a word. We use eye fixation durations (FDs) extracted from an open source eye-tracking corpus. Moreover, we propose strategies to make eye FD more effective on keyphrase extraction. The other task is to determine how to integrate human reading time into keyphrase extraction models. We propose two novel neural network models. The first is a model in which the human reading time is used as the ground truth of the attention mechanism. In the second model, we use human reading time as the external feature. Quantitative and qualitative experiments show that our proposed models yield better performance than the baseline models on two microblog datasets.
    Source
    Journal of the Association for Information Science and Technology. 72(2021) no.5, S.611-626
  8. Zhang, Y.; Zheng, G.; Yan, H.: Bridging information and communication technology and older adults by social network : an action research in Sichuan, China (2023) 0.02
    0.019329447 = product of:
      0.05154519 = sum of:
        0.030249555 = weight(_text_:use in 1089) [ClassicSimilarity], result of:
          0.030249555 = score(doc=1089,freq=4.0), product of:
            0.12644777 = queryWeight, product of:
              3.0620887 = idf(docFreq=5623, maxDocs=44218)
              0.041294612 = queryNorm
            0.23922569 = fieldWeight in 1089, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.0620887 = idf(docFreq=5623, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1089)
        0.015778005 = weight(_text_:of in 1089) [ClassicSimilarity], result of:
          0.015778005 = score(doc=1089,freq=16.0), product of:
            0.06457475 = queryWeight, product of:
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.041294612 = queryNorm
            0.24433708 = fieldWeight in 1089, product of:
              4.0 = tf(freq=16.0), with freq of:
                16.0 = termFreq=16.0
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1089)
        0.0055176322 = product of:
          0.0110352645 = sum of:
            0.0110352645 = weight(_text_:on in 1089) [ClassicSimilarity], result of:
              0.0110352645 = score(doc=1089,freq=2.0), product of:
                0.090823986 = queryWeight, product of:
                  2.199415 = idf(docFreq=13325, maxDocs=44218)
                  0.041294612 = queryNorm
                0.121501654 = fieldWeight in 1089, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  2.199415 = idf(docFreq=13325, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=1089)
          0.5 = coord(1/2)
      0.375 = coord(3/8)
    
    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
  9. 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) 0.02
    0.01880253 = product of:
      0.05014008 = sum of:
        0.019324033 = weight(_text_:of in 2808) [ClassicSimilarity], result of:
          0.019324033 = score(doc=2808,freq=24.0), product of:
            0.06457475 = queryWeight, product of:
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.041294612 = queryNorm
            0.2992506 = fieldWeight in 2808, product of:
              4.8989797 = tf(freq=24.0), with freq of:
                24.0 = termFreq=24.0
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2808)
        0.0110352645 = product of:
          0.022070529 = sum of:
            0.022070529 = weight(_text_:on in 2808) [ClassicSimilarity], result of:
              0.022070529 = score(doc=2808,freq=8.0), product of:
                0.090823986 = queryWeight, product of:
                  2.199415 = idf(docFreq=13325, maxDocs=44218)
                  0.041294612 = queryNorm
                0.24300331 = fieldWeight in 2808, product of:
                  2.828427 = tf(freq=8.0), with freq of:
                    8.0 = termFreq=8.0
                  2.199415 = idf(docFreq=13325, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=2808)
          0.5 = coord(1/2)
        0.019780781 = product of:
          0.039561562 = sum of:
            0.039561562 = weight(_text_:22 in 2808) [ClassicSimilarity], result of:
              0.039561562 = score(doc=2808,freq=4.0), product of:
                0.1446067 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.041294612 = queryNorm
                0.27358043 = fieldWeight in 2808, product of:
                  2.0 = tf(freq=4.0), with freq of:
                    4.0 = termFreq=4.0
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=2808)
          0.5 = coord(1/2)
      0.375 = coord(3/8)
    
    Abstract
    Internet based electronic resources are growing dramatically but there have been no empirical studies evaluating the impact of e-sources, as a whole, on formal scholarly communication. reports results of an investigation into how much e-sources have been used in formal scholarly communication, using a case study in the area of Library and Information Science (LIS) during the period 1994 to 1996. 4 citation based indicators were used in the study of the impact measurement. Concludes that, compared with the impact of print sources, the impact of e-sources on formal scholarly communication in LIS is small, as measured by e-sources cited, and does not increase significantly by year even though there is observable growth of these impact across the years. It is found that periodical format is related to the rate of citing e-sources, articles are more likely to cite e-sources than are print priodical articles. However, once authors cite electronic resource, there is no significant difference in the number of references per article by periodical format or by year. Suggests that, at this stage, citing e-sources may depend on authors rather than the periodical format in which authors choose to publish
    Date
    30. 1.1999 17:22:22
    Source
    Journal of information science. 24(1998) no.4, S.241-254
  10. Zhang, Y.; Broussard, R.; Ke, W.; Gong, X.: Evaluation of a scatter/gather interface for supporting distinct health information search tasks (2014) 0.02
    0.01822006 = product of:
      0.048586827 = sum of:
        0.021389665 = weight(_text_:use in 1261) [ClassicSimilarity], result of:
          0.021389665 = score(doc=1261,freq=2.0), product of:
            0.12644777 = queryWeight, product of:
              3.0620887 = idf(docFreq=5623, maxDocs=44218)
              0.041294612 = queryNorm
            0.1691581 = fieldWeight in 1261, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.0620887 = idf(docFreq=5623, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1261)
        0.017640345 = weight(_text_:of in 1261) [ClassicSimilarity], result of:
          0.017640345 = score(doc=1261,freq=20.0), product of:
            0.06457475 = queryWeight, product of:
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.041294612 = queryNorm
            0.27317715 = fieldWeight in 1261, product of:
              4.472136 = tf(freq=20.0), with freq of:
                20.0 = termFreq=20.0
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1261)
        0.00955682 = product of:
          0.01911364 = sum of:
            0.01911364 = weight(_text_:on in 1261) [ClassicSimilarity], result of:
              0.01911364 = score(doc=1261,freq=6.0), product of:
                0.090823986 = queryWeight, product of:
                  2.199415 = idf(docFreq=13325, maxDocs=44218)
                  0.041294612 = queryNorm
                0.21044704 = fieldWeight in 1261, product of:
                  2.4494898 = tf(freq=6.0), with freq of:
                    6.0 = termFreq=6.0
                  2.199415 = idf(docFreq=13325, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=1261)
          0.5 = coord(1/2)
      0.375 = coord(3/8)
    
    Abstract
    Web search engines are important gateways for users to access health information. This study explored whether a search interface based on the Bing API and enabled by Scatter/Gather, a well-known document-clustering technique, can improve health information searches. Forty participants without medical backgrounds were randomly assigned to two interfaces: a baseline interface that resembles typical web search engines and a Scatter/Gather interface. Both groups performed two lookup and two exploratory health-related tasks. It was found that the baseline group was more likely to rephrase queries and less likely to access general-purpose sites than the Scatter/Gather group when completing exploratory tasks. Otherwise, the two groups did not differ in behavior and task performance, with participants in the Scatter/Gather group largely overlooking the features (key words, clusters, and the recluster function) designed to facilitate the exploration of semantic relationships between information objects, a potentially useful means for users in the rather unfamiliar domain of health. The results suggest a strong effect of users' mental models of search on their use of search interfaces and a high cognitive cost associated with using the Scatter/Gather features. It follows that novel features of a search interface should not only be compatible with users' mental models but also provide sufficient affordance to inform users of how they can be used. Compared with the interface, tasks showed more significant impacts on search behavior. In future studies, more effort should be devoted to identify salient features of health-related information needs.
    Source
    Journal of the Association for Information Science and Technology. 65(2014) no.5, S.1028-1041
  11. Zhang, Y.: Scholarly use of Internet-based electronic resources (2001) 0.02
    0.016911605 = product of:
      0.06764642 = sum of:
        0.044457585 = weight(_text_:use in 5212) [ClassicSimilarity], result of:
          0.044457585 = score(doc=5212,freq=6.0), product of:
            0.12644777 = queryWeight, product of:
              3.0620887 = idf(docFreq=5623, maxDocs=44218)
              0.041294612 = queryNorm
            0.35158852 = fieldWeight in 5212, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              3.0620887 = idf(docFreq=5623, maxDocs=44218)
              0.046875 = fieldNorm(doc=5212)
        0.023188837 = weight(_text_:of in 5212) [ClassicSimilarity], result of:
          0.023188837 = score(doc=5212,freq=24.0), product of:
            0.06457475 = queryWeight, product of:
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.041294612 = queryNorm
            0.3591007 = fieldWeight in 5212, product of:
              4.8989797 = tf(freq=24.0), with freq of:
                24.0 = termFreq=24.0
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.046875 = fieldNorm(doc=5212)
      0.25 = coord(2/8)
    
    Abstract
    By Internet resources Zhang means any electronic file accessible by any Internet protocol. Their usage is determined by an examination of the citations to such sources in a nine-year sample of four print and four electronic LIS journals, by a survey of editors of these journals, and by a survey of scholars with "in press" papers in these journals. Citations were gathered from Social Science Citation Index and manually classed as e-sources by the format used. All authors with "in press" papers were asked about their use and opinion of Internet sources and for any suggestions for improvement. Use of electronic sources is heavy and access is very high. Access and ability explain most usage while satisfaction was not significant. Citation of e-journals increases over the eight years. Authors report under citation of e-journals in favor of print equivalents. Traditional reasons are given for citing and not citing, but additional reasons are also present for e-journals.
    Source
    Journal of the American Society for Information Science and technology. 52(2001) no.8, S.628-654
  12. 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
    0.014856692 = product of:
      0.039617848 = sum of:
        0.02011309 = weight(_text_:of in 993) [ClassicSimilarity], result of:
          0.02011309 = score(doc=993,freq=26.0), product of:
            0.06457475 = queryWeight, product of:
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.041294612 = queryNorm
            0.31146988 = fieldWeight in 993, product of:
              5.0990195 = tf(freq=26.0), with freq of:
                26.0 = termFreq=26.0
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.0390625 = fieldNorm(doc=993)
        0.0055176322 = product of:
          0.0110352645 = sum of:
            0.0110352645 = weight(_text_:on in 993) [ClassicSimilarity], result of:
              0.0110352645 = score(doc=993,freq=2.0), product of:
                0.090823986 = queryWeight, product of:
                  2.199415 = idf(docFreq=13325, maxDocs=44218)
                  0.041294612 = queryNorm
                0.121501654 = fieldWeight in 993, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  2.199415 = idf(docFreq=13325, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=993)
          0.5 = coord(1/2)
        0.013987125 = product of:
          0.02797425 = sum of:
            0.02797425 = weight(_text_:22 in 993) [ClassicSimilarity], result of:
              0.02797425 = score(doc=993,freq=2.0), product of:
                0.1446067 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.041294612 = queryNorm
                0.19345059 = fieldWeight in 993, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=993)
          0.5 = coord(1/2)
      0.375 = coord(3/8)
    
    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
  13. Zhang, Y.: Complex adaptive filtering user profile using graphical models (2008) 0.01
    0.014386265 = product of:
      0.03836337 = sum of:
        0.025048172 = weight(_text_:retrieval in 2445) [ClassicSimilarity], result of:
          0.025048172 = score(doc=2445,freq=2.0), product of:
            0.124912694 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.041294612 = queryNorm
            0.20052543 = fieldWeight in 2445, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.046875 = fieldNorm(doc=2445)
        0.0066940407 = weight(_text_:of in 2445) [ClassicSimilarity], result of:
          0.0066940407 = score(doc=2445,freq=2.0), product of:
            0.06457475 = queryWeight, product of:
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.041294612 = queryNorm
            0.103663445 = fieldWeight in 2445, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.046875 = fieldNorm(doc=2445)
        0.006621159 = product of:
          0.013242318 = sum of:
            0.013242318 = weight(_text_:on in 2445) [ClassicSimilarity], result of:
              0.013242318 = score(doc=2445,freq=2.0), product of:
                0.090823986 = queryWeight, product of:
                  2.199415 = idf(docFreq=13325, maxDocs=44218)
                  0.041294612 = queryNorm
                0.14580199 = fieldWeight in 2445, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  2.199415 = idf(docFreq=13325, maxDocs=44218)
                  0.046875 = fieldNorm(doc=2445)
          0.5 = coord(1/2)
      0.375 = coord(3/8)
    
    Abstract
    This article explores how to develop complex data driven user models that go beyond the bag of words model and topical relevance. We propose to learn from rich user specific information and to satisfy complex user criteria under the graphical modelling framework. We carried out a user study with a web based personal news filtering system, and collected extensive user information, including explicit user feedback, implicit user feedback and some contextual information. Experimental results on the data set collected demonstrate that the graphical modelling approach helps us to better understand the complex domain. The results also show that the complex data driven user modelling approach can improve the adaptive information filtering performance. We also discuss some practical issues while learning complex user models, including how to handle data noise and the missing data problem.
    Footnote
    Beitrag in einem Themenheft "Adaptive information retrieval"
  14. Trace, C.B.; Zhang, Y.; Yi, S.; Williams-Brown, M.Y.: Information practices around genetic testing for ovarian cancer patients (2023) 0.01
    0.014274012 = product of:
      0.038064033 = sum of:
        0.021389665 = weight(_text_:use in 1071) [ClassicSimilarity], result of:
          0.021389665 = score(doc=1071,freq=2.0), product of:
            0.12644777 = queryWeight, product of:
              3.0620887 = idf(docFreq=5623, maxDocs=44218)
              0.041294612 = queryNorm
            0.1691581 = fieldWeight in 1071, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.0620887 = idf(docFreq=5623, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1071)
        0.011156735 = weight(_text_:of in 1071) [ClassicSimilarity], result of:
          0.011156735 = score(doc=1071,freq=8.0), product of:
            0.06457475 = queryWeight, product of:
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.041294612 = queryNorm
            0.17277241 = fieldWeight in 1071, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1071)
        0.0055176322 = product of:
          0.0110352645 = sum of:
            0.0110352645 = weight(_text_:on in 1071) [ClassicSimilarity], result of:
              0.0110352645 = score(doc=1071,freq=2.0), product of:
                0.090823986 = queryWeight, product of:
                  2.199415 = idf(docFreq=13325, maxDocs=44218)
                  0.041294612 = queryNorm
                0.121501654 = fieldWeight in 1071, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  2.199415 = idf(docFreq=13325, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=1071)
          0.5 = coord(1/2)
      0.375 = coord(3/8)
    
    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
  15. Dang, Y.; Zhang, Y.; Chen, H.; Hu, P.J.-H.; Brown, S.A.; Larson, C.: Arizona Literature Mapper : an integrated approach to monitor and analyze global bioterrorism research literature (2009) 0.01
    0.012678035 = product of:
      0.05071214 = sum of:
        0.037047986 = weight(_text_:use in 2943) [ClassicSimilarity], result of:
          0.037047986 = score(doc=2943,freq=6.0), product of:
            0.12644777 = queryWeight, product of:
              3.0620887 = idf(docFreq=5623, maxDocs=44218)
              0.041294612 = queryNorm
            0.29299045 = fieldWeight in 2943, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              3.0620887 = idf(docFreq=5623, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2943)
        0.013664153 = weight(_text_:of in 2943) [ClassicSimilarity], result of:
          0.013664153 = score(doc=2943,freq=12.0), product of:
            0.06457475 = queryWeight, product of:
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.041294612 = queryNorm
            0.21160212 = fieldWeight in 2943, product of:
              3.4641016 = tf(freq=12.0), with freq of:
                12.0 = termFreq=12.0
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2943)
      0.25 = coord(2/8)
    
    Abstract
    Biomedical research is critical to biodefense, which is drawing increasing attention from governments globally as well as from various research communities. The U.S. government has been closely monitoring and regulating biomedical research activities, particularly those studying or involving bioterrorism agents or diseases. Effective surveillance requires comprehensive understanding of extant biomedical research and timely detection of new developments or emerging trends. The rapid knowledge expansion, technical breakthroughs, and spiraling collaboration networks demand greater support for literature search and sharing, which cannot be effectively supported by conventional literature search mechanisms or systems. In this study, we propose an integrated approach that integrates advanced techniques for content analysis, network analysis, and information visualization. We design and implement Arizona Literature Mapper, a Web-based portal that allows users to gain timely, comprehensive understanding of bioterrorism research, including leading scientists, research groups, institutions as well as insights about current mainstream interests or emerging trends. We conduct two user studies to evaluate Arizona Literature Mapper and include a well-known system for benchmarking purposes. According to our results, Arizona Literature Mapper is significantly more effective for supporting users' search of bioterrorism publications than PubMed. Users consider Arizona Literature Mapper more useful and easier to use than PubMed. Users are also more satisfied with Arizona Literature Mapper and show stronger intentions to use it in the future. Assessments of Arizona Literature Mapper's analysis functions are also positive, as our subjects consider them useful, easy to use, and satisfactory. Our results have important implications that are also discussed in the article.
    Source
    Journal of the American Society for Information Science and Technology. 60(2009) no.7, S.1466-1485
  16. Zhang, Y.; Kudva, S.: E-books versus print books : readers' choices and preferences across contexts (2014) 0.01
    0.0117461635 = product of:
      0.046984654 = sum of:
        0.030249555 = weight(_text_:use in 1335) [ClassicSimilarity], result of:
          0.030249555 = score(doc=1335,freq=4.0), product of:
            0.12644777 = queryWeight, product of:
              3.0620887 = idf(docFreq=5623, maxDocs=44218)
              0.041294612 = queryNorm
            0.23922569 = fieldWeight in 1335, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.0620887 = idf(docFreq=5623, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1335)
        0.0167351 = weight(_text_:of in 1335) [ClassicSimilarity], result of:
          0.0167351 = score(doc=1335,freq=18.0), product of:
            0.06457475 = queryWeight, product of:
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.041294612 = queryNorm
            0.25915858 = fieldWeight in 1335, product of:
              4.2426405 = tf(freq=18.0), with freq of:
                18.0 = termFreq=18.0
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1335)
      0.25 = coord(2/8)
    
    Abstract
    With electronic book (e-book) sales and readership rising, are e-books positioned to replace print books? This study examines the preference for e-books and print books in the contexts of reading purpose, reading situation, and contextual variables such as age, gender, education level, race/ethnicity, income, community type, and Internet use. In addition, this study aims to identify factors that contribute to e-book adoption. Participants were a nationally representative sample of 2,986 people in the United States from the Reading Habits Survey, conducted by the Pew Research Center's Internet & American Life Project (http://pewinternet.org/Shared-Content/Data-Sets/2011/December-2011--Reading-Habits.aspx). While the results of this study support the notion that e-books have firmly established a place in people's lives, due to their convenience of access, e-books are not yet positioned to replace print books. Both print books and e-books have unique attributes and serve irreplaceable functions to meet people's reading needs, which may vary by individual demographic, contextual, and situational factors. At this point, the leading significant predictors of e-book adoption are the number of books read, the individual's income, the occurrence and frequency of reading for research topics of interest, and the individual's Internet use, followed by other variables such as race/ethnicity, reading for work/school, age, and education.
    Source
    Journal of the Association for Information Science and Technology. 65(2014) no.8, S.1695-1706
  17. Zhang, Y.: Using the Internet for survey research : a case study (2000) 0.01
    0.011711466 = product of:
      0.046845865 = sum of:
        0.03422346 = weight(_text_:use in 4294) [ClassicSimilarity], result of:
          0.03422346 = score(doc=4294,freq=2.0), product of:
            0.12644777 = queryWeight, product of:
              3.0620887 = idf(docFreq=5623, maxDocs=44218)
              0.041294612 = queryNorm
            0.27065295 = fieldWeight in 4294, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.0620887 = idf(docFreq=5623, maxDocs=44218)
              0.0625 = fieldNorm(doc=4294)
        0.012622404 = weight(_text_:of in 4294) [ClassicSimilarity], result of:
          0.012622404 = score(doc=4294,freq=4.0), product of:
            0.06457475 = queryWeight, product of:
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.041294612 = queryNorm
            0.19546966 = fieldWeight in 4294, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.0625 = fieldNorm(doc=4294)
      0.25 = coord(2/8)
    
    Abstract
    The Internet provides opportunities to conduct surveys more efficiently and effectively than traditional means. This article reviews previous studies that use the Internet for survey research. It discusses the methodological issues and problems associated with this nes approach. By presenting a case study, it seeks possible solutions to some of the problems, and explores the potential the Internet can offer to survey researchers
    Source
    Journal of the American Society for Information Science. 51(2000) no.1, S.57-68
  18. Chen, H.; Zhang, Y.; Houston, A.L.: Semantic indexing and searching using a Hopfield net (1998) 0.01
    0.0112825725 = product of:
      0.04513029 = sum of:
        0.025048172 = weight(_text_:retrieval in 5704) [ClassicSimilarity], result of:
          0.025048172 = score(doc=5704,freq=2.0), product of:
            0.124912694 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.041294612 = queryNorm
            0.20052543 = fieldWeight in 5704, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.046875 = fieldNorm(doc=5704)
        0.02008212 = weight(_text_:of in 5704) [ClassicSimilarity], result of:
          0.02008212 = score(doc=5704,freq=18.0), product of:
            0.06457475 = queryWeight, product of:
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.041294612 = queryNorm
            0.3109903 = fieldWeight in 5704, product of:
              4.2426405 = tf(freq=18.0), with freq of:
                18.0 = termFreq=18.0
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.046875 = fieldNorm(doc=5704)
      0.25 = coord(2/8)
    
    Abstract
    Presents a neural network approach to document semantic indexing. Reports results of a study to apply a Hopfield net algorithm to simulate human associative memory for concept exploration in the domain of computer science and engineering. The INSPEC database, consisting of 320.000 abstracts from leading periodical articles was used as the document test bed. Benchmark tests conformed that 3 parameters: maximum number of activated nodes; maximum allowable error; and maximum number of iterations; were useful in positively influencing network convergence behaviour without negatively impacting central processing unit performance. Another series of benchmark tests was performed to determine the effectiveness of various filtering techniques in reducing the negative impact of noisy input terms. Preliminary user tests conformed expectations that the Hopfield net is potentially useful as an associative memory technique to improve document recall and precision by solving discrepancies between indexer vocabularies and end user vocabularies
    Source
    Journal of information science. 24(1998) no.1, S.3-18
    Theme
    Semantisches Umfeld in Indexierung u. Retrieval
  19. Zhang, Y.: Dimensions and elements of people's mental models of an information-rich Web space (2010) 0.01
    0.011097466 = product of:
      0.044389863 = sum of:
        0.021389665 = weight(_text_:use in 4098) [ClassicSimilarity], result of:
          0.021389665 = score(doc=4098,freq=2.0), product of:
            0.12644777 = queryWeight, product of:
              3.0620887 = idf(docFreq=5623, maxDocs=44218)
              0.041294612 = queryNorm
            0.1691581 = fieldWeight in 4098, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.0620887 = idf(docFreq=5623, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4098)
        0.023000197 = weight(_text_:of in 4098) [ClassicSimilarity], result of:
          0.023000197 = score(doc=4098,freq=34.0), product of:
            0.06457475 = queryWeight, product of:
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.041294612 = queryNorm
            0.35617945 = fieldWeight in 4098, product of:
              5.8309517 = tf(freq=34.0), with freq of:
                34.0 = termFreq=34.0
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4098)
      0.25 = coord(2/8)
    
    Abstract
    Although considered proxies for people to interact with a system, mental models have produced limited practical implications for system design. This might be due to the lack of exploration of the elements of mental models resulting from the methodological challenge of measuring mental models. This study employed a new method, concept listing, to elicit people's mental models of an information-rich space, MedlinePlus, after they interacted with the system for 5 minutes. Thirty-eight undergraduate students participated in the study. The results showed that, in this short period of time, participants perceived MedlinePlus from many different aspects in relation to four components: the system as a whole, its content, information organization, and interface. Meanwhile, participants expressed evaluations of or emotions about the four components. In terms of the procedural knowledge, an integral part of people's mental models, only one participant identified a strategy more aligned to the capabilities of MedlinePlus to solve a hypothetical task; the rest planned to use general search and browse strategies. The composition of participants' mental models of MedlinePlus was consistent with that of their models of information-rich Web spaces in general.
    Source
    Journal of the American Society for Information Science and Technology. 61(2010) no.11, S.2206-2218
  20. 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
    0.008781112 = product of:
      0.035124447 = sum of:
        0.016396983 = weight(_text_:of in 5245) [ClassicSimilarity], result of:
          0.016396983 = score(doc=5245,freq=12.0), product of:
            0.06457475 = queryWeight, product of:
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.041294612 = queryNorm
            0.25392252 = fieldWeight in 5245, product of:
              3.4641016 = tf(freq=12.0), with freq of:
                12.0 = termFreq=12.0
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.046875 = fieldNorm(doc=5245)
        0.018727465 = product of:
          0.03745493 = sum of:
            0.03745493 = weight(_text_:on in 5245) [ClassicSimilarity], result of:
              0.03745493 = score(doc=5245,freq=16.0), product of:
                0.090823986 = queryWeight, product of:
                  2.199415 = idf(docFreq=13325, maxDocs=44218)
                  0.041294612 = queryNorm
                0.4123903 = fieldWeight in 5245, product of:
                  4.0 = tf(freq=16.0), with freq of:
                    16.0 = termFreq=16.0
                  2.199415 = idf(docFreq=13325, maxDocs=44218)
                  0.046875 = fieldNorm(doc=5245)
          0.5 = coord(1/2)
      0.25 = coord(2/8)
    
    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.
    Source
    Journal of the Association for Information Science and Technology. 70(2019) no.5, S.448-461

Years