Abstract To date, most studies in scientometric map and track the main topics in a knowledge domain by measuring publications in core journals or keyword searches in databases. The present study instead proposes a novel metrics in which a knowledge domain is mapped and tracked via articles that cite the same openly accessible dataset. We retrieved 1,537 journal articles citing the National Longitudinal Study of Adolescent to Adult Health (Add‐Health) as the basis for an investigation of the major research topics associated with this dataset and how they evolved over time. To identify the primary research interests associated with the dataset, co‐word network modularity analysis was used. Another novel aspect of this study is that it juxtaposes the research topics identified by the co‐word approach with those generated by topic modeling: an approach that complements network modularity analysis, and allows for cross‐referencing between the results of these two methods. Keyness analysis was also performed to identify significant keywords in different time periods, which enables tracing of research interests in Add‐Health as they evolve. The methodological implications of using data citation as the basis for delineating a knowledge domain and techniques for its mapping are also discussed.

Abstract In this commentary, the authors, an international group data curation researchers and educators, reflect on some of the challenges and opportunities for data curation in the wake of the COVID‐19 pandemic. We focus on some topics of particular interest to the information science community: data infrastructures for scholarly communication and research, the politicization of data curation and visualization for public‐facing “dashboards,” and human subjects research and policies. We conclude with some areas of opportunity and need, including broader and richer data curation education in the information schools, the establishment of better data management policy implementations by research funders, the award of formal academic credit for data curation activities and data sharing, and engagement in cooperative action around data ethics and security.

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 article is to examine, among all the available online sources, which online sources laypeople select to address their health‐related information needs, and whether or how much the severity of a health condition influences their selection. Twenty‐four participants were recruited individually, and each was asked (using a retrieval system called HIS) to search for information regarding a severe health condition and a mild health condition, respectively. The selected online health information sources were automatically captured by the HIS system and classified at both the website and webpage levels. Participants' selection behavior patterns were then plotted across the whole information‐seeking process. Our results demonstrate that laypeople's source selection fluctuates during the health information‐seeking process, and also varies by the severity of health conditions. 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.

The importance of open government data is often associated with increased public trust, civic engagement, and accountable administrations. While there is a myriad of benefits, the existing literature suggests that many open government datasets lack accessibility and usability for diverse users. This study seeks to explore what contextual information users require when they access these datasets. Using mixed methods, we aim to discover the challenges of accessing data, and the necessary contextual information needed by the users to overcome these challenges. As the outcome of this study, we propose a framework called "Data Guides", which is composed of the identified important contextual information. In future work, we will test the effectiveness of the Data Guide in aiding users' accessing and understanding open government data.

From a national security viewpoint, a higher degree of cyber autonomy is crucial to reduce the reliance on external, oftentimes untrustworthy entities, in order to achieve better resilience against adversaries. To probe into the concept of government cyber autonomy, this study examines the external dependency of public-facing government websites across the world's major industrialized, Group of Seven (G7) countries. Over a two-year period, we measured HTTPS adoption rates, the autonomy status of CAs, and the autonomy status of CPs on G7 government websites. We find that approximately 85% of web resources loaded by G7 government sites originate from the United States. By reviewing policy documents and surveying technicians who maintain government websites, we identify four significant forces that can influence the degree of a government's autonomy, including government mandates on HTTPS adoption, website development outsourcing, the citizens' fear of large-scale surveillance, and user confusion. Because a government websites are considered critical information infrastructures, we expect this study to raise awareness of their complex dependency, thereby reducing the risk of blindly trusting external entities when using critical government services.

URL redirection is a popular technique that automatically navigates users to an intended destination webpage with- out user awareness. However, such a seemingly advantageous feature may offer inadequate protection from security vulnerabilities unless every redirection is performed over HTTPS. Even worse, as long as the final redirection to a website is performed over HTTPS, the browser's URL bar indicates that the website is secure regardless of the security of prior redirections, which may provide users with a false sense of security. This paper reports a well-rounded investigation to analyze the wellness of URL redirection security. As an initial large-scale investigation, we screened the integrity and consistency of URL redirections for the Alexa top one million (1M) websites, and further examined 10,000 (10K) websites with their login features. Our results suggest that 1) the majority (83.3% in the 1M dataset and 78.6% in the 10K dataset) of redirection trails among web- sites that support only HTTPS are vulnerable to attacks, and 2) current incoherent practices (e.g., naked domains and www subdomains being redirected to different destinations with varying security levels) undermine the security guarantees provided by HTTPS and HSTS.

The increasing popularity of academic social networking sites (ASNSs) requires studies on the usage of ASNSs among scholars and evaluations of the effectiveness of these ASNSs. However, it is unclear whether current ASNSs have fulfilled their design goal, as scholars' actual online interactions on these platforms remain unexplored. To fill the gap, this article presents a study based on data collected from ResearchGate. Adopting a mixed‐method design by conducting qualitative content analysis and statistical analysis on 1,128 posts collected from ResearchGate Q&A, we examine how scholars exchange information and resources, and how their practices vary across three distinct disciplines: library and information services, history of art, and astrophysics. Our results show that the effect of a questioner's intention (i.e., seeking information or discussion) is greater than disciplinary factors in some circumstances. Across the three disciplines, responses to questions provide various resources, including experts' contact details, citations, links to Wikipedia, images, and so on. We further discuss several implications of the understanding of scholarly information exchange and the design of better academic social networking interfaces, which should stimulate scholarly interactions by minimizing confusion, improving the clarity of questions, and promoting scholarly content management.

Although there are a number of social networking services that specifically target scholars, little has been published about the actual practices and the usage of these so‐called academic social networking services (ASNSs). To fill this gap, we explore the populations of academics who engage in social activities using an ASNS; as an indicator of further engagement, we also determine their various motivations for joining a group in ASNSs. Using groups and their members in Mendeley as the platform for our case study, we obtained 146 participant responses from our online survey about users' common activities, usage habits, and motivations for joining groups. Our results show that (a) participants did not engage with social‐based features as frequently and actively as they engaged with research‐based features, and (b) users who joined more groups seemed to have a stronger motivation to increase their professional visibility and to contribute the research articles that they had read to the group reading list. Our results generate interesting insights into Mendeley's user populations, their activities, and their motivations relative to the social features of Mendeley. We also argue that further design of ASNSs is needed to take greater account of disciplinary differences in scholarly communication and to establish incentive mechanisms for encouraging user participation.

Voice search offers users with a new search experience: instead of typing, users can vocalize their search queries. However, due to voice input errors (such as speech recognition errors and improper system interruptions), users need to frequently reformulate queries to handle the incorrectly recognized queries. We conducted user experiments with native English speakers on their query reformulation behaviors in voice search and found that users often reformulate queries with both lexical and phonetic changes to previous queries. In this paper, we first characterize and analyze typical voice input errors in voice search and users' corresponding reformulation strategies. Then, we evaluate the impacts of typical voice input errors on users' search progress and the effectiveness of different reformulation strategies on handling these errors. This study provides a clearer picture on how to further improve current voice search systems.

The proliferation of Location-based Social Networks (LBSNs) has been rapid during the last year due to the number of novel services they can support. The main interaction between users in an LBSN is location sharing, which builds the spatial component of the system. The majority of the LBSNs make use of the notion of check-in, to enable users to volunteeringly share their whereabouts with their peers and the system. The flow of this spatial information is unidirectional and originates from the users' side. Given that currently there is no infrastructure in place for detecting fake checkins, the quality of the spatial information plane of an LBSN is solely based on the honesty of the users. In this paper, we seek to raise the awareness of the community for this problem, by identifying and discussing the effects of the presence of fake location information. We further present a preliminary design of a fake check-in detection scheme, based on location-proofs. Our initial simulation results show that if we do not consider the infrastructural constraints, location-proofs can form a viable technical solution.