Explicitly integrating MeSH thesaurus help into health information retrieval systems: An empirical user study

When consumers search for health information, a major obstacle is their unfamiliarity with the medical terminology. Even though medical thesauri such as the Medical Subject Headings (MeSH) and related tools (e.g., the MeSH Browser) were created to help consumers find medical term definitions, the lack of direct and explicit integration of these help tools into a health retrieval system prevented them from effectively achieving their objectives. To explore this issue, we conducted an empirical study with two systems: One is a simple interface system supporting query-based searching; the other is an augmented system with two new components supporting MeSH term searching and MeSH tree browsing. A total of 45 subjects were recruited to participate in the study. The results indicated that the augmented system is more effective than the simple system in terms of improving user-perceived topic familiarity and question–answer performance, even though we did not find users spend more time on the augmented system. The two new MeSH help components played a critical role in participants’ health information retrieval and were found to allow them to develop new search strategies. The findings of the study enhanced our understanding of consumers’ search behaviors and shed light on the design of future health information retrieval systems.     

Intelligent topic selection for low-cost information retrieval evaluation: A New perspective on deep vs. shallow judging

While test collections provide the cornerstone for Cranfield-based evaluation of information retrieval (IR) systems, it has become practically infeasible to rely on traditional pooling techniques to construct test collections at the scale of today’s massive document collections (e.g., ClueWeb12’s 700M+ Webpages). This has motivated a flurry of studies proposing more cost-effective yet reliable IR evaluation methods. In this paper, we propose a new intelligent topic selection method which reduces the number of search topics (and thereby costly human relevance judgments) needed for reliable IR evaluation. To rigorously assess our method, we integrate previously disparate lines of research on intelligent topic selection and deep vs. shallow judging (i.e., whether it is more cost-effective to collect many relevance judgments for a few topics or a few judgments for many topics). While prior work on intelligent topic selection has never been evaluated against shallow judging baselines, prior work on deep vs. shallow judging has largely argued for shallowed judging, but assuming random topic selection. We argue that for evaluating any topic selection method, ultimately one must ask whether it is actually useful to select topics, or should one simply perform shallow judging over many topics? In seeking a rigorous answer to this over-arching question, we conduct a comprehensive investigation over a set of relevant factors never previously studied together: 1) method of topic selection; 2) the effect of topic familiarity on human judging speed; and 3) how different topic generation processes (requiring varying human effort) impact (i) budget utilization and (ii) the resultant quality of judgments. Experiments on NIST TREC Robust 2003 and Robust 2004 test collections show that not only can we reliably evaluate IR systems with fewer topics, but also that: 1) when topics are intelligently selected, deep judging is often more cost-effective than shallow judging in evaluation reliability; and 2) topic familiarity and topic generation costs greatly impact the evaluation cost vs. reliability trade-off. Our findings challenge conventional wisdom in showing that deep judging is often preferable to shallow judging when topics are selected intelligently.     

基于互补性理论的信息技术投资绩效研究

摘要：随着公司将大量资金投入信息技术,研究人员和企业界对于信息技术投资价值问题给予越来越多的关注。已有的研究表明,信息技术投资并没有导致公司绩效的提高,存在所谓的信息技术投资“生产率悖论”问题。在影响信息技术价值实现的各种因素中,人力资本的因素是一种非常重要的因素。信息技术投资价值的实现离不开高质量的人力资本相配合。信息资本与人力资本之间的互补性可能对公司绩效产生影响。本文以互补性理论为基础,利用上市公司的财务数据,实证检验了信息资本、人力资本以及二者交互性对公司绩效的影响。     

Bipolar queries in textual information retrieval: A new perspective

A new concept of a bipolar query against collections of textual documents, i.e. in the context of information retrieval (IR), is introduced using recent developments in bipolar information modeling and bipolar database queries. Specifically, a particular approach to bipolar queries with an explicit “and possibly” type of an aggregation operator is used. An effective and efficient processing of such bipolar queries using standard IR data structures is briefly discussed. The bipolar queries proposed combine a flexibility provided by fuzzy logic with a more sophisticated representation of user preferences and intentions. This combination can make the search of vast resources of textual document, notably those available via the Internet, more intelligent.     

Mapping user interest into hyper-spherical space: A novel POI recommendation method

Point-of-interest (POI) recommendation helps users quickly filter out irrelevant POI by considering the spatio-temporal factor. In this paper, we address the problem of check-in preference modeling in POI recommendation, and propose a novel POI recommendation method that depicts user preference by constructing unique hypersphere interest model for each user. Different from existing works, we have done three innovative work. (1) We build a check-in graph and adopt DeepWalk algorithm to learn POI embedding, further aggregating them to obtain a hypersphere interest space with an interest center and interest radius. (2) We established a stacked neural network module by a bidirectional LSTM, a self-attention and a memory network, to grasp memory features contained in check-in histories. (3) We proposed a novel candidate POI filter method that updates ranking score by evaluating the Euclidean distance between the vectors of candidate POI and interest center. We evaluate the performance of our method on the four real-world check-in datasets constructed from Foursquare. The comparison between our method and six baselines demonstrates the outstanding performance on various measurements. Compared to the best baseline method, our method achieves about 50% performance improvement on NDCG. In terms of MRR, Precision and Recall, our method achieves about 37%, 21% and 9% performance improvement over the best baseline method. Further ablation experiments verified the importance and effectiveness of the hypersphere interest model, as removing this component caused significant performance degradation.     

A new perspective of BDA and information quality from final users of information: A multiple study approach

Although organizational factors related to big data analytics (BDA) and its performance have been studied extensively, the number of failed BDA projects continues to rise. The quality of BDA information is a commonly cited factor in explanations for such failures and could prove key to improving project performance. Using the resource-based view (RBV) lens, data analytics literature, business strategy control, and an empirical setup of two studies based on marketing and information technology managerial data, we draw on the dimensions of the balanced scorecard (BSC) as an integrating framework of BDA organizational factors. Specifically, we tested a model –from two different perspectives– that would explain information quality through analytical talent and organizations' data plan alignment. Results showed that both managers have a different understanding of what information quality is. The characteristics that make marketing a better informer of information quality are identified. In addition, hybrid (embedded) type analyst placements are seen to achieve better performance. Moreover, we add greater theoretical rigour by incorporating the moderating effect of the use of big data analytics in companies. Finally, the BSC provided a greater causal understanding of the resources and capabilities within a data strategy.     

Determinants of mHealth success: An empirical investigation of the user perspective

Mobile health (mHealth) applications have become an important tool to support public health, especially in times of increased health awareness in the midst of the COVID-19 pandemic. However, there is still uncertainty about what factors determine successful mHealth services from the users’ perspective. Based on the results of a systematic literature review, a qualitative content analysis of available apps and semi-structured user and expert interviews, we derive a structural model with antecedents on user attitudes towards mHealth and user satisfaction with the mHealth application. These variables determine users’ intention to continue using the application and their intention to recommend it to others. For verification, we tested the model with a sample of 249 German mHealth users from the “MyFitnessPal” community using structural equation modelling and found that all derived path relations have significant coefficients.     

Integration of user profiles: models and experiments in information retrieval

One difficult problem in information retrieval (IR) is the proper interpretation of user queries. It is extremely hard for users to express their information needs in a specific yet exhaustive way. In an effort to alleviate this problem, two theoretical models have been proposed to utilize user characteristics maintained in the form of a user profile. Although the idea of integrating user profiles into an IR system is intuitively appealing, and the models seem viable, no research to date has established a foundation for the roles of user profiles in such a system. Aiming at the investigation of the roles of user profiles, therefore, this study first identifies and extends various query/profile interaction models to provide a ground upon which the investigation can be undertaken. From a continuum of models characterized on the basis of interaction types, metrics, and parameters, nearly 400 models are chosen to investigate the “model space.” New measures are developed based on the notion of user satisfaction/frustration. In addition, three different criteria are used to guide users in making judgments on the quality of retrieved items. Analysis of the data obtained from the experiments shows that, for a wide variety of criteria and metrics, there are always some query/profile interaction models that outperform the query alone model. In addition, preferable characteristics for different criteria are identified in terms of interaction types, parameters, and metrics.     

Defining information—II: A quantitative evaluation of the information content of documents

For the quantitative determination of the information content (IC) of documents, two requisite parameters were defined. First, the information unit (IU) as the smallest unit consistent with our previous definition of information, and second, its connectivity (C), namely, the extent to which it is connected with other information units in the document. The total quantity of information contained therein is then a function of the number of information units, as well as their connectivities.     

粘着信息与用户创新工具箱:一个研究综述

创新源理论与应用是企业创新理论中一个非常重要的研究领域 ,而粘着信息和用户创新工具箱的研究在创新源理论与应用中占据举足轻重的地位。以麻省理工学院冯·希普尔为主要代表的国外创新专家在粘着信息和用户创新工具箱等方面的研究取得了突破性的进展 ,有力地推动了创新源理论与应用的发展。本文详尽而深入地对他们在粘着信息和用户创新工具箱等方面的研究进行了评述 ,并为这些理论的进一步发展和完善指出了研究方向     

Putting to (information) work: A Stengersian perspective on how information technologies and people influence information practices

Instead of merely subscribing to an unspecific inseparability in the co-constitution or mangle of information technologies and human-actors, there is a need for conceptual tools to describe and explicate the mechanics of how the enmeshment of technologies and human-beings is occurring in information contexts: how information technologies are both setting standards of the social conduct of information practices, and how people are using information technologies to regulate the social process. Building on an empirical study of human-technology relations in the context of archaeological information work, this article discusses how the imaginary of putting Stengers to work can make a contribution to such an end. Stengers describes an ideal system of human-actors and technology working seamlessly —World-as-Clock—that is unattainable but can serve as a benchmark and a lens for understanding frictions and discrepancies in the cohesion of the two.     

Reflink: A microcomputer information retrieval and evaluation system

Reflink is a system based on sophisticated information retrieval techniques which is housed in a small, inexpensive microcomputer. The paper describes the system, along with its predecessor, REFLES. Such systems are then placed in a conceptual framework involving stable and dynamic information, and several illustrations of multiple uses are presented.     

Cultivating the sense of belonging and motivating user participation in virtual communities: A social capital perspective

Virtual communities (VCs) are attracting more attention as they provide a platform for people to share experiences and knowledge, which may further impact their purchase decisions. From a social capital perspective, this study investigated factors that cultivate a VC member's sense of belonging and their effects on facilitating his participation in the VC in terms of the intentions to get and share experiences and knowledge. The results indicated that three factors that relate to three dimensions of social capital in the VC – familiarity with members in the VC from the structural dimension, perceived similarity with other members from the cognitive dimension, and trust in other members from the relational dimension – are all positively related to the sense of belonging, which affects intentions to get and share knowledge and mediates the relationships between social capital factors and a VC member's intentions to participate.     

基于信息立法视角的政府信息公开制度研究

我国正在起草制定政府信息公开条例和法律制度。许多国家和地区、国际机构和组织都以各种法律形式将信息公开制度法律化,并赋予公民以法定的获取信息的权利。本文在探讨世界信息公开立法现状和发展趋势基础上,总结了信息公开制度的基本要素,并在借鉴国外司法经验基础上对我国信息公开立法提出建议。     

Intention to disclose personal information via mobile applications: A privacy calculus perspective

This study aimed to investigate the issue of consumer intention to disclose personal information via mobile applications (apps). Drawing on the literature of privacy calculus theory, this research examined the factors that influence the trade-off decision of receiving perceived benefits and being penalized with perceived risks through the calculus lens. In particular, two paths of the direct effects on perceived benefits and risks that induce the ultimate intention to disclose personal information via mobile apps were proposed and empirically tested. The analysis showed that self-presentation and personalized services positively influence consumers’ perceived benefits, which in turn positively affects the intention to disclose personal information. Perceived severity and perceived control serve as the direct antecedents of perceived risks that negatively affect the intention of consumers to disclose personal information. Compared with the perceived risks, the perceived benefits more strongly influence the intention to disclose personal information. This study extends the literature on privacy concerns to consumer intention to disclose personal information by theoretically developing and empirically testing four hypotheses in a research model. Results were validated in the mobile context, and implications and discussions were presented.     

Advancing social media derived information messaging and management: A multi-mode development perspective

With global reach of over 2 billion active users, the evolution of Social Media (SM) systems has provided organizations with sophisticated tools and technologies for delivering business objectives. Importantly, while marketers and public relations experts have taken leading positions in promotion and advancement of SM, project managers are often tasked with delivering SM systems. In this study, a sample of 127 project managers were asked to evaluate and recommend modes of SM development for six diverse firms using a four-part taxonomy. The results show that firms of varying size can employ narrowly focused and low cost SM development modes to meet their business objectives, with well-resourced firms able to use experimental modes to deliver widespread and higher cost ‘listen and learn’ SM systems. Alternatively, in addition to achieving groundswell promotions and broader business marketing and sales influencing objectives, firms that engage in large scale SM developments can document and implement SM best practices and apply multi-organizational collaborations required for information exchange, customer feedback and experience sharing. These managerial perspectives expose the intrinsic connections between SM systems and information messaging and management within firms. The article builds further into cumulative studies directed at SM systems construction, deployment, and firm capability affordances.     

CLAIRE: A combinatorial visual analytics system for information retrieval evaluation

Information Retrieval (IR) develops complex systems, constituted of several components, which aim at returning and optimally ranking the most relevant documents in response to user queries. In this context, experimental evaluation plays a central role, since it allows for measuring IR systems effectiveness, increasing the understanding of their functioning, and better directing the efforts for improving them. Current evaluation methodologies are limited by two major factors: (i) IR systems are evaluated as “black boxes”, since it is not possible to decompose the contributions of the different components, e.g., stop lists, stemmers, and IR models; (ii) given that it is not possible to predict the effectiveness of an IR system, both academia and industry need to explore huge numbers of systems, originated by large combinatorial compositions of their components, to understand how they perform and how these components interact together.We propose a Combinatorial visuaL Analytics system for Information Retrieval Evaluation (CLAIRE) which allows for exploring and making sense of the performances of a large amount of IR systems, in order to quickly and intuitively grasp which system configurations are preferred, what are the contributions of the different components and how these components interact together.The CLAIRE system is then validated against use cases based on several test collections using a wide set of systems, generated by a combinatorial composition of several off-the-shelf components, representing the most common denominator almost always present in English IR systems. In particular, we validate the findings enabled by CLAIRE with respect to consolidated deep statistical analyses and we show that the CLAIRE system allows the generation of new insights, which were not detectable with traditional approaches.     

The PRET A Rapporter framework: Evaluating digital libraries from the perspective of information work

The strongest tradition of IR systems evaluation has focused on system effectiveness; more recently, there has been a growing interest in evaluation of Interactive IR systems, balancing system and user-oriented evaluation criteria. In this paper we shift the focus to considering how IR systems, and particularly digital libraries, can be evaluated to assess (and improve) their fit with users’ broader work activities. Taking this focus, we answer a different set of evaluation questions that reveal more about the design of interfaces, user–system interactions and how systems may be deployed in the information working context. The planning and conduct of such evaluation studies share some features with the established methods for conducting IR evaluation studies, but come with a shift in emphasis; for example, a greater range of ethical considerations may be pertinent. We present the PRET A Rapporter framework for structuring user-centred evaluation studies and illustrate its application to three evaluation studies of digital library systems.     

Developing organized information displays for voluminous works: a study of user clustering behavior

This paper investigates the ways in which people group or categorize documents associated with a voluminous work to guide the construction of organized displays for information retrieval systems (IRSs). Fifty participants completed an unconstrained sorting task in which they were asked to sort into groups 47 documents associated with the voluminous work A Christmas Carol, by Charles Dickens. Participants were asked to group documents based on how similar they were to each other and such that the groups would help them to remember how to find them at a later time. Data collected from the sorting task were summarized using cluster analysis, employed to discover common groupings created by participants. Groupings discovered frequently shared physical format, language, and audience attributes.     

Recognizing fake information through a developed feature scheme: A user study of health misinformation on social media in China

This study aims at helping people recognize health misinformation on social media in China. A scheme was first developed to identify the features of health misinformation on social media based on content analysis of 482 pieces of health information from WeChat, a social media platform widely used in China. This scheme was able to identify salient features of health misinformation, including exaggeration/absolutes, induced text, claims of being unique and secret, intemperate tone or language, and statements of excessive significance and likewise. The scheme was then evaluated in a user-centred experiment to test if it is useful in identifying features of health misinformation. Forty-four participants for the experimental group and 38 participants for the control group participated and finished the experiment, which compared the effectiveness of these participants in using the scheme to identify health misinformation. The results indicate that the scheme is effective in terms of improving users’ capability in health misinformation identification. The results also indicate that the participants’ capability of recognizing misinformation in the experimental group has been significantly improved compared to those of the control group. The study provides insights into health misinformation and has implications in enhancing people's online health information literacy. It informs the development of a system that can automatically limit the spread of health misinformation. Moreover, it potentially improves users’ online health information literacy, in particular, under the circumstances of the COVID-19 pandemic.