Health misinformation presents significant challenges to public well-being, making effective debunking strategies crucial. While artificial intelligence (AI) shows potential in generating debunking texts, its persuasiveness compared to human-generated content remains underexplored. Drawing on Aristotle's three modes of persuasion, this study investigated the persuasive effectiveness of AI versus human-generated health debunking texts through three complementary studies. Our findings reveal a novel pattern: AI-generated texts significantly outperformed human texts in pathos (emotional appeal) and logos (logical argument) but underperformed in ethos (credibility), with all three dimensions serving as significant mediators of persuasiveness. More importantly, we demonstrate that source labeling effects are not uniform. While “AI-written” labels reduced perceived persuasiveness for both AI and human texts, this algorithmic aversion was attenuated when argument quality (logos) was made salient. These findings advance persuasion theory by revealing that classical rhetoric operates differently for AI versus human sources and that algorithmic aversion is context-dependent rather than universal. The results offer both theoretical insights into human-AI communication and practical guidance for deploying AI in health misinformation mitigation.
Data literacy, a multifaceted competency in working with data, has emerged as an essential skill that holds significance in both personal and professional lives. Nonetheless, there is a lack of a precise definition of data literacy, and individuals' perceptions of their data literacy have not been thoroughly investigated. This study aims to develop and validate a scale designed for measuring self-efficacy in data literacy within the context of higher education. Both exploratory and confirmatory factor analyses were conducted to determine construct validity and reliability. The resulting data literacy self-efficacy scale comprises 31 items organized into three factors: data identification, data processing, and data management and sharing. These factors represent distinct yet interconnected dimensions, highlighting the multifaceted nature of data literacy.
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 increasing role and impact of information systems in modern life calls for new types of information studies that examine sociotechnical factors at play in the development and use of information systems. This article proposes critical data modeling —the use of data modeling and systems analysis techniques to build critical interrogations of information systems—as a method for bridging between social factors and technical systems, presents the Basic Representation Model as an analytical tool for critical data modeling, and discusses the results of critical data modeling of a police arrest record dataset. The Basic Representation Model is a conceptual model of information objects that supports a detailed examination of data modeling and information representation within and across information systems, and functions as a synthesizing concept for existing critical work on information systems. Critical data modeling adds an essential complement to existing approaches to critical information studies by grounding the analysis of an information system in both the technical realities of computational systems and the social realities of our communities.
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.
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.
The cyberchondria phenomenon presents a significant health concern, yet there remains a relative scarcity of research on the formation mechanisms of cyberchondria and its longitudinal studies. Based on the RISP model and C-A-C framework, this study aimed to examine the longitudinal associations between risk perception, health anxiety, and online health information seeking (OHIS) within the context of Chinese social media. We used a three-wave longitudinal survey with 514 participants at one-month intervals starting September 2023, employing the Random Intercept Cross-Lagged Panel Model (RI-CLPM) for data analysis. At the between-person level, we solely observed significant negative associations between health anxiety and OHIS. At the within-person level, (1) positively bidirectional associations were noted among the three main variables; (2) causal relationships were identified between risk perception and OHIS, as well as between health anxiety and OHIS; (3) Health anxiety partially mediated the relationship between risk perception and OHIS, and OHIS fully mediated the relationship between health anxiety at T1 and T3; and (4) multi-group analyses showed that gender differences in RI-CLPM were significant, while age differences were insignificant. The results offer theoretical and practical insights for health practitioners, information seekers, and social media platforms.
Algorithms play a significant role in shaping our experiences of interacting with intelligent information systems but also inherit and amplify data biases, potentially leading to unfair decisions or discriminatory outcomes. This motivates us to investigate users' algorithm literacy , which covers the awareness and knowledge of algorithms and the skills to intervene in the operations of personalization algorithms when interacting with recommendation systems. Since vulnerable groups are more likely to suffer from the negative consequences of algorithmic decision-making, investigating algorithm literacy among such groups is critical. This study aims to examine older adults' algorithm literacy, who are often considered a vulnerable group and labeled as digital laggards in contemporary information society. The empirical evidence collected from 21 participants in in-depth interviews and cognitive mapping studies demonstrated that almost all participants are algorithm-aware to some extent and identified (1) three types of information and sources collected by algorithms in user understanding, (2) two paradigms of how respondents understand personalized recommendations, and (3) two sets of strategies they develop to employ algorithms for improving user experience. The findings shed light on designing human-centered intelligent information systems for unbiased personalization and developing a more inclusive AI-assisted society that equally benefits people of all ages.
The study explored college students' use of generative artificial intelligence (GenAI) tools, such as ChatGPT, for academic tasks and their perceptions and behaviors in assessing the credibility of GenAI-generated information. Semistructured interviews were conducted with 25 college students in the United States. Interview transcripts were analyzed using the qualitative content analysis method. The study identified various types of academic tasks for which students used ChatGPT, including writing, programming, and learning. Guided by two models of credibility assessment Hilligoss and Rieh (2008); Metzger (2007), six factors influencing students' motivation and ability to assess the credibility of GenAI-generated information were identified (e.g., task salience, social pressure). We also identified 9 constructs (e.g., refinedness, explainability), 5 heuristics (e.g., inter- and intrasystem consistency heuristics), and 10 cues (e.g., version and tone) used by students to assess the credibility of GenAI-generated information. This study provides theoretical and empirical findings regarding students' use of GenAI tools in the academic context and credibility evaluation of the system outputs using rich, qualitative interview data.
We argue that advances in large language models (LLMs) and generative Artificial Intelligence (AI) will diminish the value of Wikipedia, due to a withdrawal by human content producers, who will withhold their efforts, perceiving less need for their efforts and increased “AI competition.” We believe the greatest threat to Wikipedia stems from the fact that Wikipedia is a user-generated product, relying on the “selfish altruism” of its human contributors. Contributors who reduce their contribution efforts as AI pervades the platform, will thus leave Wikipedia increasingly dependent on additional AI activity. This, combined with a dynamic where readership creates authorship and readers being disintermediated, will inevitably cause a vicious cycle leading to a staling of the content and diminishing value of this venerable knowledge resource.
Throughout history, tattoos have served as a means of expressing identity, culture, and preserving information. Beyond their visual appeal, tattoos continue to be used in the modern world as a way for individuals to showcase their identity, honor and remember others, and mark significant events. In this paper, we explore the connection between tattoos and life transitions from an informational perspective. We view tattoos and the act of tattooing as a complex process that involves cognitive, physical, and emotional interactions with information on both an individual and societal level. The information experience approach aligns with this holistic and multifaceted nature of interacting with information, and we have employed this approach to structure our study. The study is based on interviews with 23 participants in Aotearoa New Zealand and highlights how tattoos serve as forms of information and mediums for comprehending and navigating life transitions. The findings reveal the role of tattoos as initiators, enablers, and resolvers of transitions, and explain how transition is experienced through tattoos. This study contributes to the understanding of tattoos as informational transitions and provides insights into their role in addressing the dissonance experienced in life transitions.
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.
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.
This paper explores collaborative information behavior in the context of highly politicized decision making. It draws upon a qualitative case study of project management of a contentious public sector infrastructure project. We noted the creation of spaces for the development and exchange of information by experts and conceptualize these as information spheres. We postulate that these were formed to bypass power-induced information behavior that excludes expert power, such as information avoidance. This approach contrasts with the expected project management and information norms, rules and behavior, however, provides a language that can be used to explain the phenomena of bounded information spaces which complement and may be used as a development of adjunct to small world's theory.
There has been a notable increase in bibliometric research studying gender in academia. This narrative review aims to organize and synthesize this extensive body of work to uncover new insights into gender disparities in science. We begin by analyzing key methodological elements, including gender assignment techniques, units of analysis, and causality issues. Next, we identify and categorize the main findings of the literature into three groups: differences in academia, causes behind these differences, and their primary consequences. Finally, we point out gaps in the literature and propose new lines of inquiry to address these gaps. These proposals include more rigorous gender assignment algorithms, fostering comparability of studies, exploring a broader range of topics, and improving the interpretability and context of results when studying gender.
Classification schemes are a key way of organizing bibliographic knowledge, yet the way that classification schemes communicate their information to classifiers receives little attention. This article takes a novel approach by exploring the visual aspects contained within classification schemes. The research uses a classification scheme analysis methodology. Three different classification scheme phenomena are discussed in terms of their visualization: hierarchy, notation, and notes. Indentation is found to be a significant—and implicit—method of communicating hierarchy to classifiers and offers intriguing solutions to the issues of transmuting from two dimensions into one. The visual elements of notation reveal a strong separation between notation and class, while the visual elements of notes illuminate a varying narrative around the position of notes in the classification scheme . A categorization system for visual elements in classification schemes is presented. Model 1 proffers visual elements as a fourth plane of classification, which extends and remodels Ranganathan's Three Planes of Work . Model 2 shows how visual elements could fit into classification scheme versioning. Ultimately, looking at visual aspects of classification schemes is a novel way of thinking about knowledge organization and can help us to better understand—and ultimately, to better use—classification schemes.
Research funding plays a crucial role in the production of knowledge, and its nature varies considerably from country to country. Numerous studies have analyzed research funding from a bibliometric perspective. However, the role of individual authors in attracting funding remains understudied, and it may be crucial for many actors. We propose a new approach that provides a more accurate picture and test it on post-Soviet countries with low scientific production. We analyze the funding sources of the most visible part of the natural sciences by focusing on the funding acknowledgments of their papers in Nature Index journals published in 2017–2021. Both the country of origin and types of sources are accounted for. Our approach reveals marked differences between traditionally used paper-level and proposed author-level funding links. The shares of funding sources measured in this way are very different, especially with regard to foreign sources and the role of specific countries. This is particularly important when studying international papers and the roles of the countries involved, even more so for the countries with lower research capacity. Utilizing a case-driven funding sources classification, we paint a rich picture of diverging post-Soviet funding landscapes, mostly driven by national grants and EU-wide programmes.
New knowledge builds upon existing foundations, which means an interdependent relationship exists between knowledge, manifested in the historical records of the scientific system for hundreds of years. By leveraging natural language processing techniques, this study introduces the Scientific Concept Navigator, an embedding-based navigation model to infer the “knowledge pathway” from the research trajectories of millions of scholars. We validate that the learned representations effectively delineate disciplinary boundaries and capture the intricate relationships between diverse concepts. Utility of the navigation space is showcased through multiple applications. Firstly, we demonstrate the multi-step analogy inferences between concepts from various disciplines. Secondly, we formulate the cross-domain conceptual dimensions of knowledge, observing the distributional shifts of 19 disciplines along these conceptual dimensions, including “Theoretical” to “Applied,” and “Societal” to “Economic,” highlighting the evolution of functional attributes across diverse domains. Lastly, by analyzing the knowledge network structure, we find that knowledge connects with shorter global pathways, and interdisciplinary concepts play a critical role in enhancing accessibility. Our framework offers a novel approach to mining knowledge inheritance pathways from extensive scientific literature, which is of great significance for understanding scientific progression patterns, tailoring scientific learning trajectories, and accelerating scientific progress.
