Search (28 results, page 1 of 2)

0.01847925 = product of:
  0.073917 = sum of:
    0.029630389 = weight(_text_:web in 1262) [ClassicSimilarity], result of:
      0.029630389 = score(doc=1262,freq=4.0), product of:
        0.096845865 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.029675366 = queryNorm
        0.3059541 = fieldWeight in 1262, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.046875 = fieldNorm(doc=1262)
    0.044286616 = product of:
      0.08857323 = sum of:
        0.08857323 = weight(_text_:mining in 1262) [ClassicSimilarity], result of:
          0.08857323 = score(doc=1262,freq=4.0), product of:
            0.16744171 = queryWeight, product of:
              5.642448 = idf(docFreq=425, maxDocs=44218)
              0.029675366 = queryNorm
            0.5289795 = fieldWeight in 1262, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              5.642448 = idf(docFreq=425, maxDocs=44218)
              0.046875 = fieldNorm(doc=1262)
      0.5 = coord(1/2)
  0.25 = coord(2/8)

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.

0.01734376 = product of:
  0.06937504 = sum of:
    0.046194486 = weight(_text_:web in 2385) [ClassicSimilarity], result of:
      0.046194486 = score(doc=2385,freq=14.0), product of:
        0.096845865 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.029675366 = queryNorm
        0.47698978 = fieldWeight in 2385, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2385)
    0.023180548 = weight(_text_:data in 2385) [ClassicSimilarity], result of:
      0.023180548 = score(doc=2385,freq=4.0), product of:
        0.093835 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.029675366 = queryNorm
        0.24703519 = fieldWeight in 2385, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2385)
  0.25 = coord(2/8)

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.

0.016233463 = product of:
  0.06493385 = sum of:
    0.020951848 = weight(_text_:web in 2445) [ClassicSimilarity], result of:
      0.020951848 = score(doc=2445,freq=2.0), product of:
        0.096845865 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.029675366 = queryNorm
        0.21634221 = fieldWeight in 2445, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.046875 = fieldNorm(doc=2445)
    0.043982 = weight(_text_:data in 2445) [ClassicSimilarity], result of:
      0.043982 = score(doc=2445,freq=10.0), product of:
        0.093835 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.029675366 = queryNorm
        0.46871632 = fieldWeight in 2445, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.046875 = fieldNorm(doc=2445)
  0.25 = coord(2/8)

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.

0.010423049 = product of:
  0.041692197 = sum of:
    0.029630389 = weight(_text_:web in 2742) [ClassicSimilarity], result of:
      0.029630389 = score(doc=2742,freq=4.0), product of:
        0.096845865 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.029675366 = queryNorm
        0.3059541 = fieldWeight in 2742, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.046875 = fieldNorm(doc=2742)
    0.012061807 = product of:
      0.024123615 = sum of:
        0.024123615 = weight(_text_:22 in 2742) [ClassicSimilarity], result of:
          0.024123615 = score(doc=2742,freq=2.0), product of:
            0.103918076 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.029675366 = 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)
  0.25 = coord(2/8)

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

0.0069016214 = product of:
  0.05521297 = sum of:
    0.05521297 = weight(_text_:web in 5071) [ClassicSimilarity], result of:
      0.05521297 = score(doc=5071,freq=20.0), product of:
        0.096845865 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.029675366 = queryNorm
        0.5701118 = fieldWeight in 5071, product of:
          4.472136 = tf(freq=20.0), with freq of:
            20.0 = termFreq=20.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5071)
  0.125 = coord(1/8)

Abstract

The academic impact advantage of Open Access (OA) is a prominent topic of debate in the library and publishing communities. Web citations have been proposed as comparable to, even replacements for, bibliographic citations in assessing the academic impact of journals. In our study, we compare Web citations to articles in an OA journal, the Journal of Computer-Mediated Communication (JCMC), and a traditional access journal, New Media & Society (NMS), in the communication discipline. Web citation counts for JCMC are significantly higher than those for NMS. Furthermore, JCMC receives significantly higher Web citations from the formal scholarly publications posted on the Web than NMS does. The types of Web citations for journal articles were also examined. In the Web context, the impact of a journal can be assessed using more than one type of source: citations from scholarly articles, teaching materials and non-authoritative documents. The OA journal has higher percentages of citations from the third type, which suggests that, in addition to the research community, the impact advantage of open access is also detectable among ordinary users participating in Web-based academic communication. Moreover, our study also proves that the OA journal has impact advantage in developing countries. Compared with NMS, JCMC has more Web citations from developing countries.

0.006877845 = product of:
  0.02751138 = sum of:
    0.017459875 = weight(_text_:web in 993) [ClassicSimilarity], result of:
      0.017459875 = score(doc=993,freq=2.0), product of:
        0.096845865 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.029675366 = queryNorm
        0.18028519 = fieldWeight in 993, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.0390625 = fieldNorm(doc=993)
    0.010051507 = product of:
      0.020103013 = sum of:
        0.020103013 = weight(_text_:22 in 993) [ClassicSimilarity], result of:
          0.020103013 = score(doc=993,freq=2.0), product of:
            0.103918076 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.029675366 = 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)
  0.25 = coord(2/8)

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

0.006146671 = product of:
  0.049173366 = sum of:
    0.049173366 = weight(_text_:data in 459) [ClassicSimilarity], result of:
      0.049173366 = score(doc=459,freq=18.0), product of:
        0.093835 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.029675366 = queryNorm
        0.52404076 = fieldWeight in 459, product of:
          4.2426405 = tf(freq=18.0), with freq of:
            18.0 = termFreq=18.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.0390625 = fieldNorm(doc=459)
  0.125 = coord(1/8)

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.

0.0058562206 = product of:
  0.046849765 = sum of:
    0.046849765 = weight(_text_:web in 2097) [ClassicSimilarity], result of:
      0.046849765 = score(doc=2097,freq=10.0), product of:
        0.096845865 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.029675366 = queryNorm
        0.48375595 = fieldWeight in 2097, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.046875 = fieldNorm(doc=2097)
  0.125 = coord(1/8)

Abstract

This article explores the effects of undergraduate students' mental models of the Web on their online searching behavior. Forty-four undergraduate students, mainly freshmen and sophomores, participated in the study. Subjects' mental models of the Web were treated as equally good styles and operationalized as drawings of their perceptions about the Web. Four types of mental models of the Web were identified based on the drawings and the associated descriptions: technical view, functional view, process view, and connection view. In the study, subjects were required to finish two search tasks. Searching behavior was measured from four aspects: navigation and performance, subjects' feelings about tasks and their own performances, query construction, and search patterns. The four mental model groups showed different navigation and querying behaviors, but the differences were not significant. Subjects' satisfaction with their own performances was found to be significantly correlated with the time to complete the task. The results also showed that the familiarity of the task to subjects had a major effect on their ways to start interaction, query construction, and search patterns.

0.005420855 = product of:
  0.04336684 = sum of:
    0.04336684 = weight(_text_:data in 244) [ClassicSimilarity], result of:
      0.04336684 = score(doc=244,freq=14.0), product of:
        0.093835 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.029675366 = queryNorm
        0.46216056 = fieldWeight in 244, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.0390625 = fieldNorm(doc=244)
  0.125 = coord(1/8)

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.

0.0040977807 = product of:
  0.032782245 = sum of:
    0.032782245 = weight(_text_:data in 964) [ClassicSimilarity], result of:
      0.032782245 = score(doc=964,freq=8.0), product of:
        0.093835 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.029675366 = queryNorm
        0.34936053 = fieldWeight in 964, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.0390625 = fieldNorm(doc=964)
  0.125 = coord(1/8)

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.

Footnote

Beitrag in einem Special issue on "Data Science in the iField".

0.0040228814 = product of:
  0.03218305 = sum of:
    0.03218305 = weight(_text_:wide in 1252) [ClassicSimilarity], result of:
      0.03218305 = score(doc=1252,freq=2.0), product of:
        0.13148437 = queryWeight, product of:
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.029675366 = queryNorm
        0.24476713 = fieldWeight in 1252, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1252)
  0.125 = coord(1/8)

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.

0.0034770824 = product of:
  0.027816659 = sum of:
    0.027816659 = weight(_text_:data in 2082) [ClassicSimilarity], result of:
      0.027816659 = score(doc=2082,freq=4.0), product of:
        0.093835 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.029675366 = queryNorm
        0.29644224 = fieldWeight in 2082, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.046875 = fieldNorm(doc=2082)
  0.125 = coord(1/8)

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.

0.0034770824 = product of:
  0.027816659 = sum of:
    0.027816659 = weight(_text_:data in 356) [ClassicSimilarity], result of:
      0.027816659 = score(doc=356,freq=4.0), product of:
        0.093835 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.029675366 = queryNorm
        0.29644224 = fieldWeight in 356, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.046875 = fieldNorm(doc=356)
  0.125 = coord(1/8)

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.

0.003086499 = product of:
  0.024691992 = sum of:
    0.024691992 = weight(_text_:web in 4098) [ClassicSimilarity], result of:
      0.024691992 = score(doc=4098,freq=4.0), product of:
        0.096845865 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.029675366 = queryNorm
        0.25496176 = fieldWeight in 4098, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4098)
  0.125 = coord(1/8)

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.

0.003086499 = product of:
  0.024691992 = sum of:
    0.024691992 = weight(_text_:web in 1261) [ClassicSimilarity], result of:
      0.024691992 = score(doc=1261,freq=4.0), product of:
        0.096845865 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.029675366 = queryNorm
        0.25496176 = fieldWeight in 1261, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1261)
  0.125 = coord(1/8)

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.

0.003086499 = product of:
  0.024691992 = sum of:
    0.024691992 = weight(_text_:web in 2218) [ClassicSimilarity], result of:
      0.024691992 = score(doc=2218,freq=4.0), product of:
        0.096845865 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.029675366 = queryNorm
        0.25496176 = fieldWeight in 2218, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2218)
  0.125 = coord(1/8)

Abstract

The quality of online health information for consumers has been a critical issue that concerns all stakeholders in healthcare. To gain an understanding of how quality is evaluated, this systematic review examined 165 articles in which researchers evaluated the quality of consumer-oriented health information on the web against predefined criteria. It was found that studies typically evaluated quality in relation to the substance and formality of content, as well as to the design of technological platforms. Attention to design, particularly interactivity, privacy, and social and cultural appropriateness is on the rise, which suggests the permeation of a user-centered perspective into the evaluation of health information systems, and a growing recognition of the need to study these systems from a social-technical perspective. Researchers used many preexisting instruments to facilitate evaluation of the formality of content; however, only a few were used in multiple studies, and their validity was questioned. The quality of content (i.e., accuracy and completeness) was always evaluated using proprietary instruments constructed based on medical guidelines or textbooks. The evaluation results revealed that the quality of health information varied across medical domains and across websites, and that the overall quality remained problematic. Future research is needed to examine the quality of user-generated content and to explore opportunities offered by emerging new media that can facilitate the consumer evaluation of health information.

0.0028975685 = product of:
  0.023180548 = sum of:
    0.023180548 = weight(_text_:data in 3758) [ClassicSimilarity], result of:
      0.023180548 = score(doc=3758,freq=4.0), product of:
        0.093835 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.029675366 = queryNorm
        0.24703519 = fieldWeight in 3758, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3758)
  0.125 = coord(1/8)

Abstract

Whereas traditional science maps emphasize citation statistics and static relationships, this paper presents a term-based method to identify and visualize the evolutionary pathways of scientific topics in a series of time slices. First, we create a data preprocessing model for accurate term cleaning, consolidating, and clustering. Then we construct a simulated data streaming function and introduce a learning process to train a relationship identification function to adapt to changing environments in real time, where relationships of topic evolution, fusion, death, and novelty are identified. The main result of the method is a map of scientific evolutionary pathways. The visual routines provide a way to indicate the interactions among scientific subjects and a version in a series of time slices helps further illustrate such evolutionary pathways in detail. The detailed outline offers sufficient statistical information to delve into scientific topics and routines and then helps address meaningful insights with the assistance of expert knowledge. This empirical study focuses on scientific proposals granted by the United States National Science Foundation, and demonstrates the feasibility and reliability. Our method could be widely applied to a range of science, technology, and innovation policy research, and offer insight into the evolutionary pathways of scientific activities.

0.0028975685 = product of:
  0.023180548 = sum of:
    0.023180548 = weight(_text_:data in 5816) [ClassicSimilarity], result of:
      0.023180548 = score(doc=5816,freq=4.0), product of:
        0.093835 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.029675366 = queryNorm
        0.24703519 = fieldWeight in 5816, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5816)
  0.125 = coord(1/8)

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.

0.0024586683 = product of:
  0.019669347 = sum of:
    0.019669347 = weight(_text_:data in 5245) [ClassicSimilarity], result of:
      0.019669347 = score(doc=5245,freq=2.0), product of:
        0.093835 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.029675366 = queryNorm
        0.2096163 = fieldWeight in 5245, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.046875 = fieldNorm(doc=5245)
  0.125 = coord(1/8)

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.

0.0021824844 = product of:
  0.017459875 = sum of:
    0.017459875 = weight(_text_:web in 2943) [ClassicSimilarity], result of:
      0.017459875 = score(doc=2943,freq=2.0), product of:
        0.096845865 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.029675366 = queryNorm
        0.18028519 = fieldWeight in 2943, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2943)
  0.125 = coord(1/8)

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.