Search (11 results, page 1 of 1)

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Abstract

Purpose People face barriers and failures in various kinds of information seeking experiences. These are often attributed to either the information seeker or the system/service they use. The purpose of this paper is to investigate how and why individuals fail to fulfill their information needs in all contexts and situations. It addresses the limitations of existing studies in examining the context of the task and information seeker's strategy and seeks to gain a holistic understanding of information seeking barriers and failures. Design/methodology/approach The primary method used for this investigation is a qualitative survey, in which 63 participants provided 208 real life examples of failures in information seeking. After analyzing the survey data, ten semi-structured interviews with another group of participants were conducted to further examine the survey findings. Data were analyzed using various theoretical frameworks of tasks, strategies, and barriers. Findings A careful examination of aspects of tasks, barriers, and strategies identified from the examples revealed that a wide range of external and internal factors caused people's failures. These factors were also caused or affected by multiple aspects of information seekers' tasks and strategies. People's information needs were often too contextual and specific to be fulfilled by the information retrieved. Other barriers, such as time constraint and institutional restrictions, also intensified the problem. Originality/value This paper highlights the importance of considering the information seeking episodes in which individuals fail to fulfill their needs in a holistic approach by analyzing their tasks, information needs, strategies, and obstacles. The modified theoretical frameworks and the coding methods used could also be instrumental for future research.

Date

20. 1.2015 18:30:22

Source

Aslib journal of information management. 69(2017) no.4, S.441-459

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Abstract

Research in clinical information retrieval has long been stymied by the lack of open resources. However, both clinical information retrieval research innovation and legitimate privacy concerns can be served by the creation of intrainstitutional, fully protected resources. In this article, we provide some principles and tools for information retrieval resource-building in the unique problem setting of patient-level information retrieval, following the tradition of the Cranfield paradigm. We further include an analysis of parallel information retrieval resources at Oregon Health & Science University and Mayo Clinic that were built on these principles.

Footnote

Beitrag in einem Special issue on biomedical information retrieval.

Source

Journal of the Association for Information Science and Technology. 68(2017) no.11, S.2636-2648

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Source

Journal of the American Society for Information Science. 36(1985) no.1, S.15-27

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Abstract

The contribution of this article is twofold. First, we present Indexing by latent Dirichlet allocation (LDI), an automatic document indexing method. Many ad hoc applications, or their variants with smoothing techniques suggested in LDA-based language modeling, can result in unsatisfactory performance as the document representations do not accurately reflect concept space. To improve document retrieval performance, we introduce a new definition of document probability vectors in the context of LDA and present a novel scheme for automatic document indexing based on LDA. Second, we propose an Ensemble Model (EnM) for document retrieval. EnM combines basic indexing models by assigning different weights and attempts to uncover the optimal weights to maximize the mean average precision. To solve the optimization problem, we propose an algorithm, which is derived based on the boosting method. The results of our computational experiments on benchmark data sets indicate that both the proposed approaches are viable options for document retrieval.

Date

12. 6.2016 21:39:22

Source

Journal of the Association for Information Science and Technology. 67(2016) no.7, S.1736-1750

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Abstract

In information seeking and retrieval research, researchers often collect data about users' behaviors to predict task characteristics and personalize information for users. The reliability of user behavior may be directly influenced by data collection methods. This article reports on a mixed-methods study examining the impact of study setting (laboratory setting vs. remote setting) and task authenticity (authentic task vs. simulated task) on users' online browsing and searching behaviors. Thirty-six undergraduate participants finished one lab session and one remote session in which they completed one authentic and one simulated task. Using log data collected from 144 task sessions, this study demonstrates that the synthetic lab study setting and simulated tasks had significant influences mostly on behaviors related to content pages (e.g., page dwell time, number of pages visited per task). Meanwhile, first-query behaviors were less affected by study settings or task authenticity than whole-session behaviors, indicating the reliability of using first-query behaviors in task prediction. Qualitative interviews reveal why users were influenced. This study addresses methodological limitations in existing research and provides new insights and implications for researchers who collect online user search behavioral data.

Source

Journal of the Association for Information Science and Technology. 73(2022) no.3, S.362-375

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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

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Abstract

The goal of ResearchGate (RG) is to help users exchange scholarly information around the world. This study drew on adaptive structuration theory (AST) to investigate the social structure of RG, which had been largely overlooked by prior research. Data were crawled from RG and results were presented based on content analysis. For the social structure embedded in RG, the most frequent updates of structural features and spirit occurred in the first two years. Six representative updates for information exchange were analyzed and the newly embedded social structures were presented. For the social structure emerging in using RG, users were more willing to answer questions than ask questions, which countered intuition. Three categories were elicited to present the purpose and expectation of questions. Users were more willing to publish publications than publish projects. Compared with reading publications and projects published by others, users seldom commented on them. For the comparison between the two social structures, this paper analyzed and compared the two social structures in terms of three types of information exchange, finding that the social structure emerging in using RG differed from that embedded in RG. We suggest that this paper could potentially help the two social structures of RG promote the optimization of each other.

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Abstract

Information from microblogs is gaining increasing attention from researchers interested in analyzing fluctuations in stock markets. Behavioral financial theory draws on social psychology to explain some of the irrational behaviors associated with financial decisions to help explain some of the fluctuations. In this study we argue that social media users who demonstrate an interest in finance can offer insights into ways in which irrational behaviors may affect a stock market. To test this, we analyzed all the data collected over a 3-month period in 2011 from Tencent Weibo (one of the largest microblogging websites in China). We designed a social influence (SI)-based Tencent finance-related moods model to simulate investors' irrational behaviors, and designed a Tencent Moods-based Stock Trend Analysis (TM_STA) model to detect correlations between Tencent moods and the Hushen-300 index (one of the most important financial indexes in China). Experimental results show that the proposed method can help explain the data fluctuation. The findings support the existing behavioral financial theory, and can help to understand short-term rises and falls in a stock market. We use behavioral financial theory to further explain our findings, and to propose a trading model to verify the proposed model.

Source

Journal of the Association for Information Science and Technology. 70(2019) no.9, S.1000-1013

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Abstract

The purpose of this paper is to determine semantic types for tags in social tagging systems. In social tagging systems, the determination of the semantic type of tags plays an important role in tag classification, increasing the semantic information of tags and establishing mapping relations between tagged resources and a normed ontology. The research reported in this paper constructs the semantic type library that is needed based on the Unified Medical Language System (UMLS) and FrameNet and determines the semantic type of selected tags that have been pretreated via direct matching using the Semantic Navigator tool, the Semantic Type Word Sense Disambiguation (STWSD) tools in UMLS, and artificial matching. And finally, we verify the feasibility of the determination of semantic type for tags by empirical analysis.

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Source

Journal of the American Society for Information Science and Technology. 62(2011) no.4, S.643-653

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Source

Journal of the Association for Information Science and Technology. 74(2023) no.1, S.115-127