Journal of the Association for Information Science and Technology最新文献

In the process of author name disambiguation (AND), varying characteristics and noise of different blocks significantly impact disambiguation performance. In this paper, we propose a block-based adaptive hyperparameter optimization method that assigns optimal hyperparameters to each block without altering the original AND model structure. Based on this, a random forest model is trained using the optimized results to fit the relationship between the block's data features and its optimal hyperparameters, thereby enabling the prediction of hyperparameters for new blocks. Empirical studies on 6 state-of-the-art AND algorithms, 11 public datasets, and a manually labeled dataset of China's information and communication technology (ICT) industry patents demonstrate that the proposed method significantly outperforms the original algorithms across multiple standard performance evaluation metrics (Cluster F1/Pairwise F1, B-Cubed F1, and K metrics). The results of the random forest regression indicate that the selected 16 features effectively predict the optimal hyperparameters. Further analysis reveals a power-law relationship between relative block size and both relative performance and relative optimized performance across all datasets and evaluation metrics, and the relative performance improvement of the adaptive hyperparameter optimization algorithm is particularly significant for smaller blocks. These findings provide theoretical support and practical guidance for the development of AND algorithms.

The iSchools represent an international community of over 130 university faculties. They have a shared interest in research and teaching about information. The findings reported here provide insight into the perspectives and visions of the deans and directors of 75 iSchools. Findings show that (1) attention to people, information, and technology represents shared values; and (2) the global identity of the community blends diversity and inclusiveness. The diversity and inclusiveness of the iSchools requires balancing academic traditions with innovations. The current leaders use this balance to the community's identity as well as Information Science scholarship as a discipline.

Chronic illness represents a transition for both patients and their family members although transitions and information behavior changes have largely been explored from an individual perspective. Illness-related transitions may be undertaken individually or collectively, but little is known about how family information networks change in the face of either transition type. Therefore, we conducted a longitudinal, mixed-methods study of information networks with 28 families managing HIV or diabetes. Methods included qualitative interviews, surveys, and social network analysis. Findings revealed that transitions were common among study families, with collective transitions more common than individual ones. Network size changed more among families undergoing collective transition versus those with individual or no transitions. Collective transition families experienced slightly more tie strength increases than individual transition families. More families undergoing collective transitions had illness peers in the family network than individual or no-transition families. Reciprocal information sharing was also more common among illness peers. Findings support a distinction between individual and collective transitions and study of information network changes in each context. Future research should further characterize the drivers and dynamics of collective and individual transitions and related information behavior, while investigating how information systems and services can help.

Scholarly journals have been de-nationalizing and anglicizing their names for the past six decades in order to gain international visibility and facilitate their indexation in major international databases. Using the Web of Science, we analyzed the historical evolution of this trend and its geography, showing that it has been particularly concentrated in a few countries at different periods of time. Then, we evaluated how title changes have affected the evolution of the journals' language of publication, authorship, readership, and impact. The acceleration of the trend toward the de-nationalization and anglicization of journal titles coincided with the rise of discourses on internationalization in the 1980s and the growing use, a decade later, of quantitative indicators in research evaluation, above all the impact factor. In general, this rebranding strategy of scholarly journals resulted in a higher visibility in the global market of scientific publications, leading to a more internationalized authorship and readership, but to the detriment of the use of national languages.

Relations between terms in texts have long been studied in linguistics and specialized knowledge domains, especially when occurring in educational materials like textbooks, where they play a crucial role in guiding instructional design and learning. Prerequisite relations (PR), which determine the sequence of presentation of domain terms, are particularly crucial for effective learning. Therefore, the authors consider them carefully when writing instructional texts. The reverse process of identifying PR within texts aims to extract the inherent knowledge structure they are based on and is a key task in the field of corpora annotation for educational knowledge modeling. Although there are tools for manual annotation, there is a need for specialized tools tailored to the unique properties of PR, enabling easy creation, analysis, and sharing of annotated datasets. In this paper, we introduce Prerequisite Relations Annotation Tool (PRAT), a novel tool designed for annotating PR based on a validated protocol. PRAT simplifies the process of capturing, analyzing, and visualizing prerequisite structures in educational texts. We outline PRAT's architecture and functionalities, emphasizing its unique features compared to existing corpora annotation tools. Through a user study involving users with diverse backgrounds, we show PRAT's effectiveness in real-world scenarios.

Hajj is the spiritual and religious journey of a lifetime for many Muslims around the world. In June 2022, the Saudi Ministry of Hajj and Umrah decided to introduce a new travel booking system that mandated pilgrims from Europe, America, Australia, and New Zealand to reserve directly their pilgrimage travels, thus bypassing the local Hajj travel operators. Additionally, Motawif was deployed just a few weeks before the 2022 Hajj. This announcement triggered a wave of panic among would-be pilgrims and resulted in an intense social media conversation. This article examines the conversation that took place on Twitter (now X) for the period following the launch of Motawif and up until the completion of the 2022 Hajj. Using computational social science methods, we undertook several analyses of the Twitterspace to understand the perceptions of the would-be pilgrims with the Motawif system. The study illuminates how forms of community organizing in online spaces (and associated information practices and discursive strategies) have contributed to reconstituting the Hajj pilgrim's identity and agency in ways rarely seen before among this community. Our findings also point to various strategies and information practices of opposition that enable connective action through solidarity and recognition of shared grievances.

In combating filter bubbles, an undesirable consequence of personalized recommendations, prior research has focused on improving algorithms to increase the diversity of the content recommended. Following a user-centered approach firmly grounded in information science, this study is dedicated to optimizing interaction patterns with algorithmic affordances, aiming to augment the diversity of the content consumed and induce favorable attitude changes. A controlled experiment was conducted on a mock personalized recommender system that provided both information and interactivity affordances, exemplified by stance labels and stance-based filters, respectively. A total of 142 participants were recruited to browse recommendations generated by the system on a specific controversial topic, and the selectivity of their information consumption behavior and the change in their attitude extremity were measured. It was found that both types of affordances were effective in reducing users' behavioral selectivity. While stance labels inhibited the consumption of pro-attitudinal information, stance-based filters facilitated the consumption of counter-attitudinal information. Furthermore, the affordances could immediately mitigate the attitude extremity of those with a higher level of algorithmic literacy. The findings not only enrich the growing body of literature on filter bubbles but also offer valuable implications for the affordance design practices of personalized recommender systems.

Sanfilippo, M. R., Zhu, X. A., & Yang, S. (2025). Sociotechnical governance of misinformation: An Annual Review of Information Science and Technology (ARIST) paper. Journal of the Association for Information Science and Technology, 76(1), 289–325. https://doi.org/10.1002/asi.24953

The author's name “Shengan Yang” should be corrected to “Shengnan Yang.”

We apologize for this error.

Being diagnosed with a chronic illness can lead to a life transition that invokes new kinds of information behavior for an individual. This qualitative study focuses on information behavior—particularly information needs, use, and barriers—during the life transitions of people diagnosed with hypothyroidism. Ten interviews were conducted in 2022 with individuals who were diagnosed with hypothyroidism. The data were analyzed using qualitative content analysis. Based on the intermediate transitions theory by I. Ruthven, 2022 (An information behavior theory of transitions. Journal of the Association for Information Science & Technology, 73(4), 579–593), the process of transition includes phases of understanding, negotiating, and resolving, as well as processes of event, engaging, enacting, and establishing. Based on the findings, information needs, use, and barriers varied according to the stage of disease and in different stages of a life transition. Interviewees had a wide range of information needs related to disease and diagnosis, treatment balance, and disease monitoring. Information use included the promotion of personal well-being through physical activity and improvement of information-seeking skills. The most significant barriers to information acquisition included communication issues with health care providers and symptoms of hypothyroidism such as fatigue and brain fog. For information providers, the results provide important knowledge on information behavior during a life transition related to a chronic illness.

The BookSampo Linked Data portal was deployed in 2011 by the Finnish Public Libraries and has today nearly 2 million annual users. Its Linked Data covers virtually all Finnish fiction literature but the data has not been used for data analyses in Digital Humanities. This paper discusses how the Knowledge Graph can be used for literary research in two ways: First, a new BookSampo 2.0 Portal user interface is presented, based on faceted semantic search with seamlessly integrated data-analytic tools for Digital Humanities research as suggested in the Sampo Model. This application makes it possible to analyze the data without programming skills. Second, the BookSampo SPARQL endpoint API can be accessed directly by SPARQL querying and scripting, using tools such as Jupyter Notebooks. The analysis results presented suggest interesting spatial, temporal, and topical trends in how the Finnish fiction literature has evolved during the last decades. The approach and tools presented in this paper can be used for analyzing literary landscapes developments in other countries as well.