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

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.

Differences in cognitive abilities affect search behaviors, but this has mostly been observed in laboratory experiments. There is limited research on how users search for information in real-world, naturalistic settings and how real-world search behaviors relate to cognitive abilities. In this study, we investigated a wide range of behavioral data captured from real-life search tasks, their association with users' cognitive abilities, and the potential for automatically inferring cognitive abilities from these data. Furthermore, we aimed to determine the data quantity and monitoring duration needed to effectively estimate cognitive abilities from naturalistic behavior. Twenty individuals with βvarying cognitive abilities participated in the experiments in which their everyday search behavior was continuously recorded for 14 days. Their cognitive ability was evaluated through standard tests conducted individually. Data consisted of over 800 h of monitoring, including 2022 queries extracted from 1,442,447 screen frames and associated operating system logs. Using these data, naturalistic search behaviors were associated with cognitive abilities, and predictive models were trained. The results showed that lower selective attention was found to be associated with longer dwelling on selected search results. Faster psychomotor speed and higher fluid intelligence were found to be associated with a greater amount of text read on selected pages. Predictive models exhibited small error rates in predicting cognitive abilities.

Many theories of human information behavior (HIB) assume that information objects are in text document format. This paper argues four important HIB theories are insufficient for describing users' search strategies for data because of assumptions about the attributes of objects that users seek. We first review and compare four HIB theories: Bates' berrypicking, Marchionni's electronic information search, Dervin's sense-making, and Meho and Tibbo's social scientist information-seeking. All four theories assume that information-seekers search for text documents. Next, we compare these theories to search behavior by analyzing Google Analytics data from the Inter-university Consortium for Political and Social Research (ICPSR). Users took direct, scenic, and orienting paths when searching for data. We also interviewed ICPSR users (n = 20), and they said they needed dataset documentation and contextual information to find data. However, Dervin's sense-making alone cannot explain the information-seeking behaviors that we observed. Instead, what mattered most were object attributes determined by the type of information that users sought (i.e., data, not documents). We conclude by suggesting an alternative frame for building user-centered data discovery tools.

This special issue of the “Journal of the Association for Information Science and Technology” examines conceptual models as products of, and tools for, critical inquiry in Information Science (IS). The papers included in this issue present diverse perspectives on how conceptual models impact sociotechnical systems, spanning topics such as knowledge organization, representation, and information system design. Key themes include the intersection of model development with ethical considerations, the historical and future implications of conceptual modeling decisions, and the potential for conceptual models to address issues of power, representation, and justice in emerging technologies. This introduction situates the contributions within broader discussions of conceptual modeling in IS and highlights the field's unique approach to reflexive critique and sociotechnical analysis.

Integrating diverse cues from metadata to make sense of retrieved data during relevance evaluation is a crucial yet challenging task for data searchers. However, this integrative task remains underexplored, impeding the development of effective strategies to address metadata's shortcomings in supporting this task. To address this issue, this study proposes the “Integrative Use of Metadata for Data Sense-Making” (IUM-DSM) model. This model provides an initial framework for understanding the integrative tasks performed by data searchers, focusing on their integration patterns and associated challenges. Experimental data were analyzed using an interpretable deep learning-based prediction approach to validate this model. The findings offer preliminary support for the model, revealing that data searchers engage in integrative tasks to utilize metadata effectively for data sense-making during relevance evaluation. They construct coherent mental representations of retrieved data by integrating systematic and heuristic cues from metadata through two distinct patterns: within-category integration and across-category integration. This study identifies key challenges: within-category integration entails comparing, classifying, and connecting systematic or heuristic cues, while across-category integration necessitates considerable effort to integrate cues from both categories. To support these integrative tasks, this study proposes strategies for mitigating these challenges by optimizing metadata layouts and developing intelligent data retrieval systems.

After the launch of Google Scholar older papers experienced an increase in their citations, a finding consistent with a reduction in search costs and introduction of ranking algorithms. I employ this observation to examine how recombination of science takes place in the era of online search platforms. The findings show that as papers become more discoverable, their knowledge is diffused beyond their own broad field. Results are mixed when examining knowledge diffusion within the same field. The results contribute to the ongoing debate of narrowing of science. While there might a general reduction in recombination of knowledge across distant fields over the last decades, online search platforms are not the culprits.