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

Communicating insights from data effectively requires design skills, technical knowledge, and experience. Data must be accurately represented with aesthetically pleasing visuals and engaging text to effectively communicate to the intended audience. Data storytelling has received much attention lately, but as of yet, it does not have a theoretical and practical foundation in information science. A data story adds context, narrative, and structure to the visual representation of data, providing audiences with character, plot, and a holistic experience of narrative. This paper proposes a methodological approach to transform a data visualization into a data story based on the Data-Information-Knowledge-Wisdom (DIKW) pyramid and the S-DIKW Framework. Starting from the bottom of the pyramid, the proposed approach defines a strategy to represent insights extracted from data. Data is then turned into information by identifying character(s) facing a problem, adding textual and graphic content; information is turned into knowledge by organizing what happens as a plot. Finally, a call to wise action—always informed by cultural and community values—completes the storytelling transformation to create a data story. This article contributes to the theoretical understanding of data stories as emerging information forms, supporting richer understandings of a story as information in the information sciences.

This review paper explores the evolution of discussions about “long-tail” scientific data in the scholarly literature. The “long-tail” concept, originally used to explain trends in digital consumer goods, was first applied to scientific data in 2007 to refer to a vast array of smaller, heterogeneous data collections that cumulatively represent a substantial portion of scientific knowledge. However, these datasets, often referred to as “long-tail data,” are frequently mismanaged or overlooked due to inadequate data management practices and institutional support. This paper examines the changing landscape of discussions about long-tail data over time, situated within broader ecosystems of research data management and the natural interplay between “big” and “small” data. The review also bridges discussions on data curation in Library & Information Science (LIS) and domain-specific contexts, contributing to a more comprehensive understanding of the long-tail concept's utility for effective data management outcomes. The review aims to provide a more comprehensive understanding of this concept, its terminological diversity in the literature, and its utility for guiding data management, overall informing current and future information science research and practice.

When using retrieval-augmented generation (RAG) to handle multi-document question answering (MDQA) tasks, it is beneficial to decompose complex queries into multiple simpler ones to enhance retrieval results. However, previous strategies always employ a one-shot approach of question decomposition, overlooking subquestions dependency problem and failing to ensure that the derived subqueries are single-hop. To overcome this challenge, we introduce a novel framework called DSRC-QCS. Decompose-solve-renewal-cycle (DSRC) is an iterative multi-hop question processing module. The key idea of DSRC involves using a unique symbol to achieve hierarchical dependency management and employing a cyclical process of question decomposition, solving, and renewal to continuously generate and resolve all single-hop subquestions. Query-chain selector (QCS) functions as a voting mechanism that effectively utilizes the reasoning process of DSRC to assess and select solutions. We compare DSRC-QCS against five RAG approaches across three datasets and three LLMs. DSRC-QCS demonstrates superior performance. Compared to the Direct Retrieval method, DSRC-QCS improves the average F1 score by 17.36% with Alpaca-7b, 10.83% with LLaMa2-Chat-7b, and 11.88% with GPT-3.5-Turbo. We also conduct ablation studies to validate the performance of both DSRC and QCS and explore factors influencing the effectiveness of DSRC. We have included all prompts in the Appendix.

As the widespread use of algorithms and artificial intelligence (AI) technologies, understanding the interaction process of human–algorithm interaction becomes increasingly crucial. From the human perspective, algorithmic awareness is recognized as a significant factor influencing how users evaluate algorithms and engage with them. In this study, a formative study identified four dimensions of algorithmic awareness: conceptions awareness (AC), data awareness (AD), functions awareness (AF), and risks awareness (AR). Subsequently, we implemented a heuristic intervention and collected data on users' algorithmic awareness and FAT (fairness, accountability, and transparency) evaluation in both pre-test and post-test stages (N = 622). We verified the dynamics of algorithmic awareness and FAT evaluation through fuzzy clustering and identified three patterns of FAT evaluation changes: “Stable high rating pattern,” “Variable medium rating pattern,” and “Unstable low rating pattern.” Using the clustering results and FAT evaluation scores, we trained classification models to predict different dimensions of algorithmic awareness by applying different machine learning techniques, namely Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), and XGBoost (XGB). Comparatively, experimental results show that the SVM algorithm accomplishes the task of predicting the four dimensions of algorithmic awareness with better results and interpretability. Its F1 scores are 0.6377, 0.6780, 0.6747, and 0.75. These findings hold great potential for informing human-centered algorithmic practices and HCI design.

The Latin-American scientific community has achieved significant progress towards gender parity, with nearly equal representation of women and men scientists. Nevertheless, women continue to be underrepresented in scholarly communication. Throughout the 20th century, Latin America established its academic circuit, focusing on research topics of regional significance. Through an analysis of scientific publications, this article explores the relationship between gender inequalities in science and the integration of Latin-American researchers into the regional and global academic circuits between 1993 and 2022. We find that women are more likely to engage in the regional circuit, while men are more active within the global circuit. This trend is attributed to a thematic alignment between women's research interests and issues specific to Latin America. Furthermore, our results reveal that the mechanisms contributing to gender differences in symbolic capital accumulation vary between circuits. Women's work achieves equal or greater recognition compared to men's within the regional circuit, but generally garners less attention in the global circuit. Our findings suggest that policies aimed at strengthening the regional academic circuit would encourage scientists to address locally relevant topics while simultaneously fostering gender equality in science.

This paper investigates the use of multimedia verification, in particular, computational tools and Open-source Intelligence (OSINT) methods, for verifying online multimedia content in the context of the ongoing wars in Ukraine and Gaza. Our study examines the workflows and tools used by several fact-checkers and journalists working at Faktisk, a Norwegian fact-checking organization. Our study showcases the effectiveness of diverse resources, including AI tools, geolocation tools, internet archives, and social media monitoring platforms, in enabling journalists and fact-checkers to efficiently process and corroborate evidence, ensuring the dissemination of accurate information. This research provides an in-depth analysis of the role of computational tools and OSINT methods for multimedia verification. It also underscores the potentials of currently available technology, and highlights its limitations while providing guidance for future development of digital multimedia verification tools and frameworks.

In this paper, we examine the role digital curation practices and practitioners played in facilitating open science (OS) initiatives amid the COVID-19 pandemic. In Summer 2023, we conducted a content analysis of available information regarding 50 OS initiatives that emerged—or substantially shifted their focus—between 2020 and 2022 to address COVID-19 related challenges. Despite growing recognition of the value of digital curation for the organization, dissemination, and preservation of scientific knowledge, our study reveals that digital curatorial work often remains invisible in pandemic OS initiatives. In particular, we find that, even among those initiatives that greatly invested in digital curation work, digital curation is seldom mentioned in mission statements, and little is known about the rationales behind curatorial choices and the individuals responsible for the implementation of curatorial strategies. Given the important yet persistent invisibility of digital curatorial work, we propose a shift in how we conceptualize digital curation from a practice that merely “adds value” to research outputs to a practice of knowledge production. We conclude with reflections on how iSchools can lead in professionalizing the field and offer suggestions for initial steps in that direction.

Information avoidance has long been in the shadow of information seeking. Variously seen as undesired, maladaptive, or even pathological, information avoidance has lacked the sustained attention and conceptualization that has been provided to other information practices. It is also, perhaps uniquely among information practices, often invoked to blame or censure those who engage in it. However, closer examination of information avoidance reveals nuanced and complex patterns of interactions with information, ones that often have positive and beneficial outcomes. We challenge the simplistic tenor of this conversation through this critical conceptual review of information avoidance. Starting from an examination of how information avoidance has been treated within information science and related disciplines, we then draw upon the various terms that have been used to describe a lack of engagement with information to establish seven core characteristics of the concept. We subsequently use this analysis to establish our definition of information avoidance as practices that moderate interaction with information by reducing the intensity of information, restricting control over information, and/or excluding information based on perceived properties. We consider the implications of this definition and its view of information avoidance as a significant information practice on information research.

In today's linguistically diverse world, managing personal information across multiple languages presents a challenge. This study engaged 16 multilingual participants to explore their user experience in the context of multilingual personal information management (MPIM), with a focus on inclusivity, universality, and equity. Addressing two main questions, the study explores the challenges users face on digital platforms in MPIM contexts and their ideal platform features. Findings highlight key issues in MPIM platform design, including unsupported languages and integration of visual aesthetics. We also identify user preferences for ideal platform features, such as language flexibility and efficient information retrieval. The study suggests the need for more inclusive, universal, and equitable platform designs that cater to the specific requirements of multilingual users. Ultimately, this study underscores the critical need for improved MPIM support and emphasizes the significance of continued exploration in this area, establishing it as a vital field of future research.

ChatGPT and other large language models (LLMs) have been successful at natural and computer language processing tasks with varying degrees of complexity. This brief communication summarizes the lessons learned from a series of investigations into its use for the complex text analysis task of research quality evaluation. In summary, ChatGPT is very good at understanding and carrying out complex text processing tasks in the sense of producing plausible responses with minimum input from the researcher. Nevertheless, its outputs require systematic testing to assess their value because they can be misleading. In contrast to simple tasks, the outputs from complex tasks are highly varied and better results can be obtained by repeating the prompts multiple times in different sessions and averaging the ChatGPT outputs. Varying ChatGPT's configuration parameters from their defaults does not seem to be useful, except for the length of the output requested.