Search (44 results, page 1 of 3)

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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

Type

a

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Abstract

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

Source

Journal of the American Society for Information Science and Technology. 61(2010) no.1, S.88-110

Type

a

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Abstract

Evaluating the quality of online health information (OHI) is a major challenge facing consumers. We designed PageGraph, an interface that displays quality indicators and associated values for a webpage, based on credibility evaluation models, the nudge theory, and existing empirical research concerning professionals' and consumers' evaluation of OHI quality. A qualitative evaluation of the interface with 16 participants revealed that PageGraph rendered the information and presentation nudges as intended. It provided the participants with easier access to quality indicators, encouraged fresh angles to assess information credibility, provided an evaluation framework, and encouraged validation of initial judgments. We then conducted a quantitative evaluation of the interface involving 60 participants using a between-subject experimental design. The control group used a regular web browser and evaluated the credibility of 12 preselected webpages, whereas the experimental group evaluated the same webpages with the assistance of PageGraph. PageGraph did not significantly influence participants' evaluation results. The results may be attributed to the insufficiency of the saliency and structure of the nudges implemented and the webpage stimuli's lack of sensitivity to the intervention. Future directions for applying nudges to support OHI evaluation were discussed.

Date

22. 6.2023 18:18:34

Source

Journal of the Association for Information Science and Technology. 74(2023) no.7, S.828-845

Type

a

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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

Type

a

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Abstract

Predicting a researcher's knowledge trajectories beyond their current foci can leverage potential inter-/cross-/multi-disciplinary interactions to achieve exploratory innovation. In this study, we present a method of diffusion-based network analytics for knowledge trajectory recommendation. The method begins by constructing a heterogeneous bibliometric network consisting of a co-topic layer and a co-authorship layer. A novel link prediction approach with a diffusion strategy is then used to capture the interactions between social elements (e.g., collaboration) and knowledge elements (e.g., technological similarity) in the process of exploratory innovation. This diffusion strategy differentiates the interactions occurring among homogeneous and heterogeneous nodes in the heterogeneous bibliometric network and weights the strengths of these interactions. Two sets of experiments-one with a local dataset and the other with a global dataset-demonstrate that the proposed method is prior to 10 selected baselines in link prediction, recommender systems, and upstream graph representation learning. A case study recommending knowledge trajectories of information scientists with topical hierarchy and explainable mediators reveals the proposed method's reliability and potential practical uses in broad scenarios.

Date

22. 6.2023 18:07:12

Source

Journal of the Association for Information Science and Technology. 74(2023) no.7, S.775-790

Type

a

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Abstract

In this opinion paper, we argue that global health crises are also information crises. Using as an example the coronavirus disease 2019 (COVID-19) epidemic, we (a) examine challenges associated with what we term "global information crises"; (b) recommend changes needed for the field of information science to play a leading role in such crises; and (c) propose actionable items for short- and long-term research, education, and practice in information science.

Source

Journal of the Association for Information Science and Technology. 71(2020) no.12, S.1419-1423

Type

a

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Abstract

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

Source

Journal of the Association for Information Science and Technology. 65(2014) no.5, S.911-927

Type

a

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Abstract

Knowledge of ovarian cancer patients' information practices around cancer genetic testing (GT) is needed to inform interventions that promote patient access to GT-related information. We interviewed 21 ovarian cancer patients and survivors who had GT as part of the treatment process and analyzed the transcripts using the qualitative content analysis method. We found that patients' information practices, manifested in their information-seeking mode, information sources utilized, information assessment, and information use, showed three distinct styles: passive, semi-active, and active. Patients with the passive style primarily received information from clinical sources, encountered information, or delegated information-seeking to family members; they were not inclined to assess information themselves and seldom used it to learn or influence others. Women with semi-active and active styles adopted more active information-seeking modes to approach information, utilized information sources beyond clinical settings, attempted to assess the information found, and actively used it to learn, educate others, or advocate GT to family and friends. Guided by the social ecological model, we found multiple levels of influences, including personal, interpersonal, organizational, community, and societal, acting as motivators or barriers to patients' information practice. Based on these findings, we discussed strategies to promote patient access to GT-related information.

Source

Journal of the Association for Information Science and Technology. 74(2023) no.11, S.1265-1281

Type

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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

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Abstract

Searches for specific factual health information constitute a significant part of consumer health information requests, but little is known about how users search for such information. This study attempts to fill this gap by observing users' behavior while using MedlinePlus to search for specific health information. Nineteen students participated in the study, and each performed 12 specific tasks. During the search process, they submitted short queries or complete questions, and they examined less than 1 result per search. Participants rarely reformulated queries; when they did, they tended to make a query more specific or more general, or iterate in different ways. Participants also browsed, primarily relying on the alphabetical list and the anatomical classification, to navigate to specific health topics. Participants overall had a positive experience with MedlinePlus, and the experience was significantly correlated with task difficulty and participants' spatial abilities. The results suggest that, to better support specific item search in the health domain, systems could provide a more "natural" interface to encourage users to ask questions; effective conceptual hierarchies could be implemented to help users reformulate queries; and the search results page should be reconceptualized as a place for accessing answers rather than documents. Moreover, multiple schemas should be provided to help users navigate to a health topic. The results also suggest that users' experience with information systems in general and health-related systems in particular should be evaluated in relation to contextual factors, such as task features and individual differences.

Source

Journal of the Association for Information Science and Technology. 65(2014) no.1, S.53-68

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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.

Source

Journal of the Association for Information Science and Technology. 66(2015) no.10, S.2071-2084

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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"

Source

Information processing and management. 44(2008) no.6, S.1886-1900

Type

a

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Abstract

Designing effective consumer health information systems requires deep understanding of the context in which the systems are being used. However, due to the elusive nature of the concept of context, few studies have made it a focus of examination. To fill this gap, we studied the context of consumer health information searching by analyzing questions posted on a social question and answer site: Yahoo! Answers. Based on the analysis, a model of context was developed. The model consists of 5 layers: demographic, cognitive, affective, situational, and social and environmental. The demographic layer contains demographic factors of the person of concern; the cognitive layer contains factors related to the current search task (specifically, topics of interest and information goals) and users' cognitive ability to articulate their needs. The affective layer contains different affective motivations and intentions behind the search. The situational layer contains users' perceptions of the current health condition and where the person is in the illness trajectory. The social and environmental layer contains users' social roles, social norms, and various information channels. Several novel system functions, including faceted search and layered presentation of results, are proposed based on the model to help contextualize and improve users' interactions with health information systems.

Source

Journal of the American Society for Information Science and Technology. 64(2013) no.6, S.1158-1172

Type

a

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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

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a

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Abstract

Twitter is utilized by many, including professional businesses and organizations; however, there are very few studies on how other entities interact with these organizations in the Twittersphere. This article presents a study that investigates tweets related to 5 major library and information science (LIS) professional organizations in the United States. This study applies a systematic tweets analysis framework, including descriptive analytics, network analytics, and co-word analysis of hashtags. The findings shed light on user engagement with LIS professional organizations and the trending discussion topics on Twitter, which is valuable for enabling more successful social media use and greater influence.

Source

Journal of the Association for Information Science and Technology. 71(2020) no.4, S.491-496

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a

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Abstract

Conversational interfaces are increasingly popular as a way of connecting people to information. With the increased generative capacity of corpus-based conversational agents comes the need to classify and filter out malevolent responses that are inappropriate in terms of content and dialogue acts. Previous studies on the topic of detecting and classifying inappropriate content are mostly focused on a specific category of malevolence or on single sentences instead of an entire dialogue. We make three contributions to advance research on the malevolent dialogue response detection and classification (MDRDC) task. First, we define the task and present a hierarchical malevolent dialogue taxonomy. Second, we create a labeled multiturn dialogue data set and formulate the MDRDC task as a hierarchical classification task. Last, we apply state-of-the-art text classification methods to the MDRDC task, and report on experiments aimed at assessing the performance of these approaches.

Source

Journal of the Association for Information Science and Technology. 72(2021) no.12, S.1477-1497

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a

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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.

Source

Journal of the Association for Information Science and Technology. 68(2017) no.8, S.1925-1939

Type

a

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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

Type

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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.

Source

Information processing and management. 44(2008) no.3, S.1076-1085

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a

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Abstract

The rapid growth of the non-English-speaking Internet population has created a need for better searching and browsing capabilities in languages other than English. However, existing search engines may not serve the needs of many non-English-speaking Internet users. In this paper, we propose a generic and integrated approach to searching and browsing the Internet in a multilingual world. Based an this approach, we have developed the Chinese Business Intelligence Portal (CBizPort), a meta-search engine that searches for business information of mainland China, Taiwan, and Hong Kong. Additional functions provided by CBizPort include encoding conversion (between Simplified Chinese and Traditional Chinese), summarization, and categorization. Experimental results of our user evaluation study show that the searching and browsing performance of CBizPort was comparable to that of regional Chinese search engines, and CBizPort could significantly augment these search engines. Subjects' verbal comments indicate that CBizPort performed best in terms of analysis functions, cross-regional searching, and user-friendliness, whereas regional search engines were more efficient and more popular. Subjects especially liked CBizPort's summarizer and categorizer, which helped in understanding search results. These encouraging results suggest a promising future of our approach to Internet searching and browsing in a multilingual world.

Footnote

Teil eines Themenheftes zu: Information seeking research

Source

Journal of the American Society for Information Science and Technology. 55(2004) no.9, S.818-831

Type

a