Showing papers in "Journal of the Association for Information Science and Technology in 2020"
TL;DR: It is argued that global health crises are also information crises and changes needed for the field of information science to play a leading role in such crises are recommended.
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.
134 citations
TL;DR: A privacy protection model for book search is presented to formulate the constraints that ideal fake queries should satisfy, and a group of plausible fake queries for each user book query is proposed to cover up the sensitive subjects behind users' queries.
Abstract: In a digital library, book search is one of the most important information services. However, with the rapid development of network technologies such as cloud computing, the server‐side of a digital library is becoming more and more untrusted; thus, how to prevent the disclosure of users' book query privacy is causing people's increasingly extensive concern. In this article, we propose to construct a group of plausible fake queries for each user book query to cover up the sensitive subjects behind users' queries. First, we propose a basic framework for the privacy protection in book search, which requires no change to the book search algorithm running on the server‐side, and no compromise to the accuracy of book search. Second, we present a privacy protection model for book search to formulate the constraints that ideal fake queries should satisfy, that is, (i) the feature similarity, which measures the confusion effect of fake queries on users' queries, and (ii) the privacy exposure, which measures the cover‐up effect of fake queries on users' sensitive subjects. Third, we discuss the algorithm implementation for the privacy model. Finally, the effectiveness of our approach is demonstrated by theoretical analysis and experimental evaluation.
86 citations
TL;DR: A consolidated (based on all prior work) and systemic (relating to the phenomenon in its entire scope) definition of the sharing economy is developed, based on the detailed analysis of definitions and explanations in 152 sources identified in a systematic literature review.
Abstract: The “sharing economy” has recently emerged as a major global phenomenon in practice and is consequently an important research topic. What, precisely, is meant by this term, “sharing economy”? The literature to date offers many, often incomplete and conflicting definitions. This makes it difficult for researchers to lead a coherent discourse, to compare findings and to select appropriate cases. Alternative terms (e.g., “collaborative consumption,” “gig economy,” and “access economy”) are a further complication. To resolve this issue, our article develops a consolidated (based on all prior work) and systemic (relating to the phenomenon in its entire scope) definition of the sharing economy. The definition is based on the detailed analysis of definitions and explanations in 152 sources identified in a systematic literature review. We identify 36 original understandings of the term “sharing economy.” Using semantic integration strategies, we consolidate 84 semantic facets in these definitions into 18 characteristics of the sharing economy. Resolving conflicts in the meaning and scope of these characteristics, we arrive at a consolidated, systemic definition. We evaluate the definition's appropriateness and applicability by applying it to cases claimed by the media to be examples of the sharing economy. This article's definition is useful for future research and discourse on the sharing economy.
64 citations
TL;DR: Using organizational learning theory to develop a conceptual framework that explains how the ISM and IR functions can be better integrated and create learning opportunities that lead to organizational security benefits.
Abstract: Digital assets of organizations are under constant threat from a wide assortment of nefarious actors. When threats materialize, the consequences can be significant. Most large organizations invest in a dedicated information security management (ISM) function to ensure that digital assets are protected. The ISM function conducts risk assessments, develops strategy, provides policies and training to define roles and guide behavior, and implements technological controls such as firewalls, antivirus, and encryption to restrict unauthorized access. Despite these protective measures, incidents (security breaches) will occur. Alongside the security management function, many organizations also retain an incident response (IR) function to mitigate damage from an attack and promptly restore digital services. However, few organizations integrate and learn from experiences of these functions in an optimal manner that enables them to not only respond to security incidents, but also proactively maneuver the threat environment. In this article we draw on organizational learning theory to develop a conceptual framework that explains how the ISM and IR functions can be better integrated. The strong integration of ISM and IR functions, in turn, creates learning opportunities that lead to organizational security benefits including: increased awareness of security risks, compilation of threat intelligence, removal of flaws in security defenses, evaluation of security defensive logic, and enhanced security response.
59 citations
TL;DR: Findings demonstrate that students lacked awareness of educational data mining and analytic practices, as well as the data on which they rely, and institutions must balance their desire to implement LA with their obligation to educate students about their analytic practices.
Abstract: Higher education institutions are continuing to develop their capacity for learning analytics (LA), which is a sociotechnical data‐mining and analytic practice. Institutions rarely inform ...
56 citations
TL;DR: Various knowledge gaps in information privacy scholarship are synthesized and a research agenda that promotes greater cross‐disciplinary collaboration within the iSchool community and beyond is proposed.
Abstract: In this position article, we synthesize various knowledge gaps in information privacy scholarship and propose a research agenda that promotes greater cross‐disciplinary collaboration within the iSchool community and beyond. We start by critically examining Westin's conceptualization of information privacy and argue for a contextual approach that holds promise for overcoming some of Westin's weaknesses. We then highlight three contextual considerations for studying privacy—digital networks, marginalized populations, and the global context—and close by discussing how these considerations advance privacy theorization and technology design.
49 citations
TL;DR: What a more responsible smart city could look like, underpinned by technological sovereignty, which is a way to use technologies to promote individual and collective autonomy and empowerment via ownership, control, and self-governance of data and technologies.
Abstract: This article explores technological sovereignty as a way to respond to anxieties of control in digital urban contexts, and argues that this may promise a more meaningful social license to operate smart cities. First, we present an overview of smart city developments with a critical focus on corporatization and platform urbanism. We critique Alphabet's Sidewalk Labs development in Toronto, which faces public backlash from the #BlockSidewalk campaign in response to concerns over not just privacy, but also lack of community consultation, the prospect of the city losing its civic ability to self-govern, and its repossession of public land and infrastructure. Second, we explore what a more responsible smart city could look like, underpinned by technological sovereignty, which is a way to use technologies to promote individual and collective autonomy and empowerment via ownership, control, and self-governance of data and technologies. To this end, we juxtapose the Sidewalk Labs development in Toronto with the Barcelona Digital City plan. We illustrate the merits (and limits) of technological sovereignty moving toward a fairer and more equitable digital society.
47 citations
TL;DR: Empirical analysis of Cabellʼs Journal Blacklist reveals substantial diversity in types and degrees of predatory publishing, including journals with questionable peer‐review systems and business models, commonly dubbed “predatory publishing.”
Abstract: The emergence of open access (OA) publishing has altered incentives and opportunities for academic stakeholders and publishers. These changes have yielded a variety of new economic and aca...
47 citations
TL;DR: It is argued that research is international, but multilingual publishing keeps locally relevant research alive with the added potential for creating impact.
Abstract: We investigate the state of multilingualism across the social sciences and humanities (SSH) using a comprehensive data set of research outputs from seven European countries (Czech Republic...
46 citations
TL;DR: This work proposes a new, automated approach that uses the whole matrix of co‐addressed topics and actors for understanding and visualizing online debates and shows the advantages of the new approach with the analysis of two data sets.
Abstract: Social media data provide increasing opportunities for the automated analysis of large sets of textual documents. So far, automated tools have been developed either to account for the social networks among participants in the debates, or to analyze the content of these debates. Less attention has been paid to mapping co‐occurrences of actors (participants) and topics (content) in online debates that can be considered as socio‐semantic networks. We propose a new, automated approach that uses the whole matrix of co‐addressed topics and actors for understanding and visualizing online debates. We show the advantages of the new approach with the analysis of two data sets: first, a large set of English‐language Twitter messages at the Rio + 20 meeting, in June 2012 (72,077 tweets), and second, a smaller data set of Dutch‐language Twitter messages on bird flu related to poultry farming in 2015–2017 (2,139 tweets). We discuss the theoretical, methodological, and substantive implications of our approach, also for the analysis of other social media data.
42 citations
TL;DR: It is found that women are much less likely than men to approve of the use of cameras using FRT in the workplace, and how the consequences of surveillance and technologies like FRT may be gendered is considered.
Abstract: Employers are increasingly using information and communication technologies to monitor employees. Such workplace surveillance is extensive in the United States, but its experience and pote...
TL;DR: It is concluded that file management is a ubiquitous, challenging, and relatively unsupported activity that invites and has received attention from several disciplines and has broad importance for topics across information science.
Abstract: Computer users spend time every day interacting with digital files and folders, including downloading, moving, naming, navigating to, searching for, sharing, and deleting them. Such file management...
TL;DR: It is argued that higher education institutions are paradigms of information fiduciaries and have a special responsibility to their students, and cases when learning analytics violate an institution's responsibility to its students are analyzed.
Abstract: Higher education institutions are mining and analyzing student data to effect educational, political, and managerial outcomes. Done under the banner of “learning analytics,” this work can—and often does—surface sensitive data and information about, inter alia, a student's demographics, academic performance, offline and online movements, physical fitness, mental wellbeing, and social network. With these data, institutions and third parties are able to describe student life, predict future behaviors, and intervene to address academic or other barriers to student success (however defined). Learning analytics, consequently, raise serious issues concerning student privacy, autonomy, and the appropriate flow of student data. We argue that issues around privacy lead to valid questions about the degree to which students should trust their institution to use learning analytics data and other artifacts (algorithms, predictive scores) with their interests in mind. We argue that higher education institutions are paradigms of information fiduciaries. As such, colleges and universities have a special responsibility to their students. In this article, we use the information fiduciary concept to analyze cases when learning analytics violate an institution's responsibility to its students.
TL;DR: In this paper, the authors argue that efforts to combat continuing gender inequalities in academia need to be informed by evidence about where differences occur, and that gender inequality is relevant as potential evidence in appointment and promotion.
Abstract: Efforts to combat continuing gender inequalities in academia need to be informed by evidence about where differences occur. Citations are relevant as potential evidence in appointment and ...
TL;DR: The results show that team size varies substantially by discipline and country, with Japan having two‐thirds more authors per article than the United Kingdom, and solo authorship associates with higher citation impact in this field.
Abstract: Research collaboration is promoted by governments and research funders, but if the relative prevalence and merits of collaboration vary internationally then different national and discipli...
TL;DR: The results by partial least squares analysis indicate that besides noneconomic benefits including self‐enhancement, social support, and entertainment, financial factors such as cost and benefit have significant influences on the perceived value of using trilateral payment‐based Q&A platforms.
Abstract: More and more social Q&A platforms are launching a new business model to monetize online knowledge. This monetizing process introduces a more complicated cost and benefit tradeoff to users, especia...
TL;DR: This literature review provides an overview of research on the information seeking and searching of users with impairments to provide an overview to both researchers and practitioners who work with any of the user groups identified.
Abstract: Information seeking and access are essential for users in all walks of life, from addressing personal needs such as finding flights to locating information needed to complete work tasks. Over the past decade or so, the general needs of people with impairments have increasingly been recognized as something to be addressed, an issue embedded both in international treaties and in state legislation. The same tendency can be found in research, where a growing number of user studies including people with impairments have been conducted. The purpose of these studies is typically to uncover potential barriers for access to information, especially in the context of inaccessible search user interfaces. This literature review provides an overview of research on the information seeking and searching of users with impairments. The aim is to provide an overview to both researchers and practitioners who work with any of the user groups identified. Some diagnoses are relatively well represented in the literature (for instance, visual impairment), but there is very little work in other areas (for instance, autism) and in some cases no work at all (for instance, aphasia). Gaps are identified in the research, and suggestions are made regarding areas where further research is needed.
TL;DR: The results indicate that consumers’ concerns changed over the 4 defined periods of the Zika virus outbreak, and consumers became more interested in the role that the government and health organizations played in the public health emergency.
Abstract: This study investigates the content of questions and responses about the Zika virus on Yahoo! Answers as a recent example of how public concerns regarding an international health issue are reflected in social media. We investigate the contents of posts about the Zika virus on Yahoo! Answers, identify and reveal subject patterns about the Zika virus, and analyze the temporal changes of the revealed subject topics over 4 defined periods of the Zika virus outbreak. Multidimensional scaling analysis, temporal analysis, and inferential statistical analysis approaches were used in the study. A resulting 2‐layer Zika virus schema, and term connections and relationships are presented. The results indicate that consumers’ concerns changed over the 4 defined periods. Consumers paid more attention to the basic information about the Zika virus, and the prevention and protection from the Zika virus at the beginning of the outbreak of the Zika virus. During the later periods, consumers became more interested in the role that the government and health organizations played in the public health emergency.
TL;DR: This study proposes a new variable of entropy to measure a tweetʼs uncertainty, an important factor influencing disaster tweetsʼ retweeting, and suggests a set of guidelines for effectively crafting disaster messages on Twitter.
Abstract: The rapid and wide dissemination of up‐to‐date, localized information is a central issue during disasters. Being attributed to the original 140‐character length, Twitter provides its users...
TL;DR: The findings of this empirical study using a large data set collected from a popular online investment community, StockTwits, show that experience diversity, participant independence, and network decentralization are all positively related to crowd performance.
Abstract: Fueled by the explosive growth of Web 2.0 and social media, online investment communities have become a popular venue for individual investors to interact with each other. Investor opinions extracted from online investment communities capture “crowd wisdom” and have begun to play an important role in financial markets. Existing research confirms the importance of crowd wisdom in stock predictions, but fails to investigate factors influencing crowd performance (that is, crowd prediction accuracy). In order to help improve crowd performance, our research strives to investigate the impact of crowd characteristics on crowd performance. We conduct an empirical study using a large data set collected from a popular online investment community, StockTwits. Our findings show that experience diversity, participant independence, and network decentralization are all positively related to crowd performance. Furthermore, crowd size moderates the influence of crowd characteristics on crowd performance. From a theoretical perspective, our work enriches extant literature by empirically testing the relationship between crowd characteristics and crowd performance. From a practical perspective, our findings help investors better evaluate social sensors embedded in user‐generated stock predictions, based upon which they can make better investment decisions.
TL;DR: It is shown that prior estimates of the effect of journal reputation on an individual article's impact are likely inflated, and an innovative proxy for individual article quality is presented: the number of citations to preprints posted on arXiv.org.
Abstract: Journals play a critical role in the scientific process because they evaluate the quality of incoming papers and offer an organizing filter for search. However, the role of journals has be...
TL;DR: In this paper, the role of privacy in the health context was examined by investigating the influence of privacy concerns and perceived benefits on individuals' acceptance of health technologies us... and examined the role privacy in health context.
Abstract: This paper examines the role of privacy in the health context by investigating the influence of privacy concerns and perceived benefits on individuals' acceptance of health technologies us...
TL;DR: The article contributes to the information behavior literature by developing a more informed understanding of both the interrelationship between the information worlds and activities of older persons, and how older persons seek and share information during disasters.
Abstract: While there is a growing body of research on information behavior during nonroutine events such as natural disasters, the research has largely neglected older persons as a specific group despite their identification as a demographic that suffers disproportionately during disasters. To address this gap, this article reports on the study of the information behavior and related activities of older persons during natural disasters. Based on a qualitative study, we draw on the theory of information worlds to study the key activities of preparing, responding, and recovering from disasters. The article contributes to the information behavior literature by developing a more informed understanding of both the interrelationship between the information worlds and activities of older persons, and how older persons seek and share information during disasters.
TL;DR: Search engines are the most frequently used among the four channels of information discussed in this study and Poisson regression indicated that individuals' channel experience, age, student status, health status, and triangulation are substantive predictors for channel selection of OHI.
Abstract: This study investigates the influence of individual and information characteristics on university students' information channel selection (that is, search engines, social question & answer sites, online health websites, and social networking sites) of online health information (OHI) for three different types of search tasks (factual, exploratory, and personal experience). Quantitative data were collected via an online questionnaire distributed to students on various postgraduate programs at a large UK university. In total, 291 responses were processed for descriptive statistics, Principal Component Analysis, and Poisson regression. Search engines are the most frequently used among the four channels of information discussed in this study. Credibility, ease of use, style, usefulness, and recommendation are the key factors influencing users' judgments of information characteristics (explaining over 62% of the variance). Poisson regression indicated that individuals' channel experience, age, student status, health status, and triangulation (comparing sources) as well as style, credibility, usefulness, and recommendation are substantive predictors for channel selection of OHI.
TL;DR: An extensive survey of previous studies is conducted and a comprehensive feature framework is summarized, including text statistics, writing style, readability, article structure, network, and editing history, is summarized.
Abstract: Currently, web document repositories have been collaboratively created and edited. One of these repositories, Wikipedia, is facing an important problem: assessing the quality of Wikipedia. Existing...
TL;DR: The experimental results show that the proposed 3 different approaches to measure the semantic relatedness between 2 words can outperform state‐of‐the‐art approaches in all the selected English benchmark data sets.
Abstract: In this research, we propose 3 different approaches to measure the semantic relatedness between 2 words: (i) boost the performance of GloVe word embedding model via removing or transforming abnormal dimensions; (ii) linearly combine the information extracted from WordNet and word embeddings; and (iii) utilize word embedding and 12 linguistic information extracted from WordNet as features for Support Vector Regression. We conducted our experiments on 8 benchmark data sets, and computed Spearman correlations between the outputs of our methods and the ground truth. We report our results together with 3 state‐of‐the‐art approaches. The experimental results show that our method can outperform state‐of‐the‐art approaches in all the selected English benchmark data sets.
TL;DR: A critical‐theoretical analysis of the motivations and practices of the early scholar‐led publishers of the late 1980s and early 1990s reveals the importance that these journals placed on experimental practices, critique of commercial publishing, and the desire to reach new audiences.
Abstract: The movement for open access publishing (OA) is often said to have its roots in the scientific disciplines, having been popularized by scientific publishers and formalized through a range of top‐down policy interventions. But there is an often‐neglected prehistory of OA that can be found in the early DIY publishers of the late 1980s and early 1990s. Managed entirely by working academics, these journals published research in the humanities and social sciences and stand out for their unique set of motivations and practices. This article explores this separate lineage in the history of the OA movement through a critical‐theoretical analysis of the motivations and practices of the early scholar‐led publishers. Alongside showing the involvement of the humanities and social sciences in the formation of OA, the analysis reveals the importance that these journals placed on experimental practices, critique of commercial publishing, and the desire to reach new audiences. Understood in today's context, this research is significant for adding complexity to the history of OA, which policymakers, advocates, and publishing scholars should keep in mind as OA goes mainstream.
TL;DR: The literature of personalization in text retrieval is surveyed, following a framework for aspects or factors that can be used for personalization, and challenges are discussed and directions for future effort are suggested.
Abstract: Personalization of information retrieval (PIR) is aimed at tailoring a search toward individual users and user groups by taking account of additional information about users besides their queries. ...
TL;DR: This study reveals laypeople's real usage of different types of online health information sources, and engenders implications to the design of search engines, as well as the development of health literacy programs.
Abstract: For laypeople, searching online health information resources can be challenging due to topic complexity and the large number of online sources with differing quality. The goal of this arti...
TL;DR: This article investigates for the first time methods to detect crisis‐related messages on social media where the type of the crisis is not known in advance, and proposes a new ensemble learning method to perform on a par with the Gradient Boosting and AdaBoost ensemble learners.
Abstract: This article addresses the problem of detecting crisis‐related messages on social media, in order to improve the situational awareness of emergency services. Previous work focused on developing machine‐learning classifiers restricted to specific disasters, such as storms or wildfires. We investigate for the first time methods to detect such messages where the type of the crisis is not known in advance, that is, the data are highly heterogeneous. Data heterogeneity causes significant difficulties for learning algorithms to generalize and accurately label incoming data. Our main contributions are as follows. First, we evaluate the extent of this problem in the context of disaster management, finding that the performance of traditional learners drops by up to 40% when trained and tested on heterogeneous data vis‐á‐vis homogeneous data. Then, in order to overcome data heterogeneity, we propose a new ensemble learning method, and found this to perform on a par with the Gradient Boosting and AdaBoost ensemble learners. The methods are studied on a benchmark data set comprising 26 disaster events and four classification problems: detection of relevant messages, informative messages, eyewitness reports, and topical classification of messages. Finally, in a case study, we evaluate the proposed methods on a real‐world data set to assess its practical value.