bluuebunny/crossref_metadata_2025 huggingface dataset:
The Shape(s) of Information Practice: Using Radial Mapping Qualitatively
Authors: McKenzie Pamela J , Hicks Alison , Bronstein Jenny , Hyldegård Jette , Ruthven Ian , Widén Gunilla
Published: October 2024

Information practices comprise both seeking and avoidance. Although information practices scholars use qualitative and visual methods to understand seeking practices, they rarely do so to understand avoidance. This paper proposes a new visual method that supports revealing and explaining the complex interplay of seeking and avoidance in rich qualitative data. We introduce seven dimensions of information seeking and avoidance practice (intensity, granularity, engagement, control, relevance, quality, and timeliness). We conceptualize these visually as axes radiating outward from a central origin. We use Excel radar charts to depict these dimensions, allowing us to identify and characterize the shapes of information seeking and avoidance in everyday information practices. We apply our approach to two cases from published literature to show how mapping reveals the interplay between seeking and avoidance at one point in time and over time. We propose potential data collection and analysis applications of this method for information practices research.
Has OA Moved Past a Lingua Franca?
Authors: Moulaison‐Sandy Heather , Shelton Amanda
Published: October 2024

Open access (OA) publishing has been touted as an equalizer to access. However, English has effectively attained a status of lingua franca in science, and the extent to which OA supports cross‐language dissemination and consumption of information beyond English is not well understood. This preliminary work investigates English‐language sources as referenced in OA articles across 8 world languages in the four most common subject areas of study (i.e. Medicine; Biochemistry, Genetics, and Molecular Biology; Engineering; and Social Sciences) as indexed in Scopus in 2023. Non‐English languages and language families analyzed (i.e., Chinese, Spanish, Portuguese, and Russian) consistently included references to English language sources. Further, English‐language articles strongly favored providing references to other English‐language sources, with 17 out of 20 English‐language articles exclusively containing English‐language references. Future work will extend the current investigation, while considering the affordances and disadvantages of Scopus to access non‐English OA articles.
Cybersecurity: Putting Indigenous Peoples First
Authors: Oliver Gillian , Lawrence Chris , Timbery Narissa , Lilley Spencer , Rudolph Carsten , Whitty Monica , Saha Manika
Published: October 2024

Despite rising awareness of the critical importance of Indigenous data sovereignty principles for the empowerment of Indigenous communities, there is minimal evidence of any engagement from cybersecurity policy makers or researchers to actualise these goals. This paper reports preliminary findings from the first phase of a more extensive research programme investigating cybersecurity in relation to Indigenous communities, which analysed national cybersecurity policies in Australia, New Zealand, Canada and the United States.
Social Justice and Indoctrination: Views of Faculty Accused of Bias
Authors: Oltmann Shannon , Dowell Meghan
Published: October 2024

This study explores the discourse surrounding ideological bias in American higher education, particularly focusing on perceptions of bias against conservative viewpoints as documented by Professor Watchlist. Through interviews with individuals included on the watchlist, this research investigates the existence and nature of political bias on college campuses. The findings reveal a nuanced understanding among respondents, with many disputing the notion of systemic bias against conservative students, while also acknowledging potential bias against conservative ideas. The study highlights the complexities between academic freedom, ideological diversity, and perceptions of bias. The results contribute to ongoing debates about the role of political ideology in academia and its implications for classroom dynamics.
Stewarding Contextual Integrity in Data Services for Indigenous Scholarship
Authors: Palmer Carole L. , Sebastian Karcher , Belarde‐Lewis Miranda , Littletree Sandy , Guerrero Nestor
Published: October 2024

The CARE Principles for Indigenous Data Governance provide essential guideposts for the stewardship of Indigenous data. To put CARE into practice in libraries and repositories, resources are needed to support implementation and integration into current research data services (RDS). Based on analysis of case studies with scholars of Indigenous language and culture, this paper articulates specific Indigenous research and data practices to guide metadata work and other areas of responsibility in RDS. The cases surface the richness of relationships and the significance of accountability in the research process—demonstrating the “relational accountability” inherent in Indigenous research methods. Robust representation of relationality is essential to retaining integrity of context in metadata for Indigenous research data. We consider the practical implications of documenting relational context with current descriptive metadata approaches and challenges in achieving CARE adherent metadata, which we argue is the backbone for broader application of CARE for Indigenous RDS.
Harnessing the Power of Scientists and Livestreaming: Health Information Outreach in a Medical Library
Authors: Song Shijie , Ying Jun , Zhao Yuxiang (Chris) , Li Jinhao
Published: October 2024

Over the last two decades, medical libraries have shifted from being careful suppliers of health information for consumers and patients to taking on prominent roles in outreach initiatives. The emerging livestreaming technology provides opportunities for medical libraries to disseminate health information and facilitate dialogues between health professionals and the public. This article presents a case study of Fudan University Medical Library's innovative approach to health information outreach through livestreaming. By leveraging the expertise of health professionals and embracing the livestreaming technologies, the library successfully engaged a broad audience, surpassing the limitations of traditional outreach methods. Based on the case, the article discusses the transformative potential of livestreaming in reshaping health information outreach and promoting health.
Assessing Preregistration Deviations: A Comparative Analysis of Psychologists and Open Science Experts
Authors: Ting Yu‐Ning , Huang Pao‐Pei , Kung Li‐Fei , Huang Yu‐Wen , Jeng Wei
Published: October 2024

Undisclosed deviations between preregistration plans and published articles challenge research transparency. This study examines whether open science experts can aid in assessing such deviations by comparing their evaluations with those of psychology experts. Using content analysis, we compared assessments of 25 preregistration plans and articles, following a psychologist studying preregistration discrepancies framework. The 72.6% agreement rate suggests differences stemmed from distinct perspectives. Psychologists leveraged domain knowledge to scrutinize subtle inconsistencies, while open science experts, deeply committed to transparency principles, focused more on procedural adherence. Open science experts, unfamiliar with psychology's implicit norms, faced challenges in discerning field‐specific knowledge and interpreting ambiguities as non‐deviations due to unclear preregistration formats and ambiguous content. Targeted support and collaborative approaches engaging both expert groups could enhance preregistration adherence evaluations, balancing the open science experts' emphasis on transparent processes with the psychologists' domain expertise, ultimately strengthening research transparency and reproducibility.
Recognizing Large‐Scale AIGC on Search Engine Websites Based on Knowledge Integration and Feature Pyramid Network
Authors: Wang Fan , Wang Afeng , Pan Minghao , Deng Shengli , Qian Qianwen , Jia Ruiqi , Zheng Ruyi
Published: October 2024

The proliferation of Artificial Intelligence Generated Content (AIGC) poses significant challenges to user experience and information accuracy, especially on search engine websites(Guo et al., 2023). The current solution is to identify AIGC by machine learning algorithms or publicly available AI detection tools, whereas, machine learning(Wang & Wang, 2022) algorithms degrade in accuracy as more data is available and tools such as GPTZero perform poorly in the task of AIGC detection on social media. In this paper, we propose an EPCNN model to identify AIGC on search engine websites, which maintains good performance in large‐scale samples. The ERNIE model integrates cross‐domain knowledge and improves language understanding and generalization. We use ERNIE to extract text features, then use a feature pyramid network to capture semantic information at different levels, and finally use an end‐to‐end structure to connect ERNIE and the feature pyramid network to construct the EPCNN. Experimental results show that our proposed algorithm has high accuracy and the ability to handle large‐scale data compared with machine learning algorithms and AI detection tools.
Digital Literacy Education for Older Adults: A Scoping Review
Authors: Xu Lingran , Lam Yu Yan , Ng Davy Tsz Kit , Peng Pei , Suen Wing Lam , Lee Cameron Shum Yi , Chu Samuel Kai Wah
Published: October 2024

In the context of global aging, digital technologies are increasingly recognized as a means to address the challenges associated with aging. Although many studies have explored the relationship between older adults and digital literacy, there is a gap identifying their necessary skills, challenges, and learning motivations. This review identified 30 articles from 2014 to 2024 and delineated five areas of digital literacy skills that older adults acquire, the hurdles they face in this process, and what drives their learning efforts. The difficulties faced by older adults in learning digital literacy include unfamiliarity with technology, cognitive decline, emotional barriers, limited access to digital resources, and physical and health issues. These challenges may lead to frustration in their use of devices and apps, which may affect their willingness to learn. This study, despite a small sampling size, paves the way for future research, including evaluating online fake information and addressing safety risks.
Information Standards and Guidelines on AI: Ethical Concerns, Trustworthiness, Quality Assessment, and Human Oversight
Authors: Dickey Timothy J. , Dobreski Brian , Hlava Marjorie , Lund Brady , Needleman Mark , Zeng Marcia L.
Published: October 2024

This panel, sponsored by the ASIS&T Standards Committee, has two purposes. First, the panel gives a brief report on the most recent activities of the national and international standards in the field of information science and technologies in which the ASIS&T Standards Committee and members have been involved. More importantly, the panel will focus on several recent significant standardization efforts that address the ethical concerns, trustworthiness, quality assessment, and human oversight of artificial intelligence (AI) systems. A final open discussion on these issues will involve the audience, to highlight the benefits, challenges, and standards needed while addressing the important role of information science within our evolving society.
Who Are They? Redefining Demarcation of Research Participant Characteristics and Related Conceptualizations
Authors: Fourie Ina , Agarwal Naresh K. , Bothma Theo JD. , Mierzecka Anna , Nesset Val
Published: October 2024

In any research that involves people, whether as respondents or participants, demarcation of their characteristics is crucial for necessary context. These can include their roles (e.g., patient, caregiver), relationships (e.g., partner), and demographics such as age range, [dis]ability, ethnicity, gender, language, race, socio‐economic group or geographic region. Yet, despite this need for precision, terminology used for demarcation is often treated as intuitive (“y'know”). For example, age demarcations seem to be obvious but are often laden with vagueness, lack of standardization, and/or acknowledgement of differences in age ranges. Some demarcations might also be influenced by country norms or by disciplinary interpretations (e.g., legal vs medical). Considering the emphasis on respect for diversity and the need for rigor in research it seems timely to open a discussion of the many questions to be answered when demarcating characteristics of research participants. Using examples, this panel will explore some applications and definitions of demarcations.
Health Misinformation Research
Authors: Greyson Devon , Lundy Morgan , Rutter Sophie , Walugembe David
Published: October 2024

Health misinformation research has dramatically increased since the start of the COVID‐19 pandemic, although not all of this work has taken advantage of the rich theoretical and methodological background information science as a discipline has to contribute. This panel presentation will feature information scientists conducting health misinformation research from and in various settings, to showcase the value and range of information science approaches to health misinformation research. Each panelist will describe their work in the area of understanding and/or addressing health misinformation in institutions such as libraries and schools, health systems and interventions such as vaccination or public health promotion, or the general public in their online or offline information environments. Presentations will also highlight the ways researchers are applying information science theory, methods, and/or approaches to health misinformation topics, as well as lessons panelists have learned through this work. The panel will conclude with an interactive audience discussion that will center on the ways in which information science can help understand and address health misinformation challenges around the world.
Conceptions of Everyday Life in Information Science
Authors: Hartel Jenna , Ndumu Ana , Ocepek Melissa , Ruthven Ian
Published: October 2024

This panel examines conceptions of everyday life in Information Science. Several theories about everyday life and its information phenomena will be reviewed and analyzed for their origins, distinctions, and divergent claims. Four expert panelists who have published on these matters will encapsulate their ideas, and there will be a video interlude, as well. By design, the panel Agenda features short opening statements, leaving 40 minutes to discuss: How do existing notions of everyday life within Information Science bring information into focus in different ways? Are informational conceptions of everyday life adequate or wanting of critical re‐examination? In keeping with ASIS&T's multiperspective community, inputs will be sought from students, practitioners, first‐time conference attendees, and other groups, in turn. If, as Marcia J. Bates claims, we are “…always looking for the red thread of information in the social texture of people's lives” (1999, p. 1048) then we need to individually and collectively reflect on the nature of everyday life.
AI for Library and Information Science (AI4LIS): Towards a Research Agenda
Authors: Hofman Darra , Novin Alamir , Ghosh Souvick , Mohammadi Ehsan
Published: October 2024

This 90‐minute panel is structured to engage the ASIS&T community in discussion of the methods which LIS researchers and educators are using to interrogate, develop, understand and teach about and with artificial intelligence. In particular, this panel will invite the audience to join in an open fishbowl to surface how people in LIS are engaging with artificial intelligence, to identify the most pressing gaps and needs, and to lay the initial groundwork for an AI research agenda for LIS.
Exploring the Application of Artificial Intelligence and Machine Learning in GLAM Collections
Authors: Kim Jeonghyun , Chen Haihua , Yang Le , Simic Julia
Published: October 2024

In recent years, considerable work has been done on the application of artificial intelligence (AI) and machine learning (ML) in the realm of cultural heritage. The purpose of this panel is to address the following questions: What is the current understanding and implementation status of AI/ML in GLAM collections? What are the associated concerns and challenges in real cases? What does this entail in applying AI/ML in the field of computational archives? To address these questions, the panelists will present: 1) use cases of AI/ML technologies applied within GLAM collections, 2) findings from a systematic review of literature on AI/ML in GLAM collections, and 3) insights from semi‐structured interviews with archival practitioners on their perspectives on AI/ML. Following the panelists' presentations, an interactive discussion session will be conducted to delve deeper into the topics discussed.
Social Risks in the Era of Generative AI
Authors: Liu Xiaozhong , Lin Yu‐Ru , Jiang Zhuoren , Wu Qunfang
Published: October 2024

Generative AI (GAI) technologies have demonstrated human‐level performance on a vast spectrum of tasks. However, recent studies have also delved into the potential threats and vulnerabilities posed by GAI, particularly as they become increasingly prevalent in sensitive domains such as elections and education. Their use in politics raises concerns about manipulation and misinformation. Further exploration is imperative to comprehend the social risks associated with GAI across diverse societal contexts. In this panel, we aim to dissect the impact and risks posed by GAI on our social fabric, examining both technological and societal perspectives. Additionally, we will present our latest investigations, including the manipulation of ideologies using large language models (LLMs), the potential risk of AI self‐consciousness, the application of Explainable AI (XAI) to identify patterns of misinformation and mitigate their dissemination, as well as the influence of GAI on the quality of public discourse. These insights will serve as catalysts for stimulating discussions among the audience on this crucial subject matter, and contribute to fostering a deeper understanding of the importance of responsible development and deployment of GAI technologies.
The People behind the Research: How Three Researchers Utilized Their Diverse Positionality to Study LGBTQ+ Archives
Authors: Oltmann Shannon M. , Wagner Travis , Martell Allan
Published: October 2024

In this panel, three researchers with distinct positionalities will discuss how their unique perspectives contributed to the overall success of one research project (though with some inherent challenges). First, we review our respective positions with regards to the research project, a study of LGBTQ+ archives. Then we describe how our subjective pasts and experiences interacted as we conducted the research. With this panel, we intend to open a conversation about how one's individual background and perspective can both conflict and harmonize with others, leading to interesting differences in research approaches, methods, and analysis. This panel will explicitly include time for the audience to reflect on their own positionality with respect to their research; we anticipate a richly nuanced discussion.
Provocations on iSchools and Librarianship: New Priorities for LIS Forward
Authors: Palmer Carole L. , Renear Allen , Dempsey Lorcan , Hall Tracie D. , Marchionini Gary , Subramaniam Mega , Coward Chris
Published: October 2024

For the last twenty years, the field of library and information science (LIS) has been evolving within the context of iSchools. Now, organizationally melded with other information fields, LIS is better positioned than ever to synergize expertise across disciplines to benefit librarianship. However, the recent LIS Forward position paper asserts that dynamics are changing within iSchools in ways that can strengthen or diminish LIS, surfacing critical tensions and issuing recommendations for the iSchool community. This panel assembles thought leaders to share new perspectives on the provocations of the position paper and to further advance the conversation with the ASIS&T community. The group will consider themes of diversification, competition, and identity and explore two urgent professional concerns—Black librarianship and AI. A moderated discussion will be documented to inform future engagement on charting directions, investments, and grand challenges for the field of LIS and the profession.
Exploring Some Impacts of Advances in Artificial Intelligence: A Social Informatics Approach
Authors: Rosenbaum Howard , Gumusel Ece , Sanfilippo Madelyn Rose , Sweeney Miriam , Sawyer Steve , Zhou Kyrie Zhixuan
Published: October 2024

This panel explores the evolving relationship between technology and society through the lens of Social Informatics (SI) research, with a specific focus on the integration of Artificial Intelligence (AI) into social and organizational settings. Experts will discuss how AI adoption across various sectors influences social dynamics, cultural norms, and ethical considerations for a range of stakeholders. Key themes include responsible AI development, user privacy concerns, and addressing biases in AI algorithms. Through interdisciplinary insights, the panel aims to engage the audience in a lively discussion of AI's impact on society, exploring current challenges and opportunities at the intersection of SI and AI research.
Human Subjectivity in Information Practice and AI Governance
Authors: Wang Fang , Zhang Chao , Yang Shengnan , Liu Xiaozhong , Liu Ying‐Hsang
Published: October 2024

The rise of Artificial Intelligence (AI) introduces a notable tension in the realm of traditional, human‐centric information practices, where human subjectivity has been pivotal in both influencing and being influenced by our interactions with information. An excessive reliance on AI distances humans from practices, potentially diminishing human subjectivity. Additionally, as AI takes on roles once exclusively human, it might constrict opportunities for personal growth and the cultivation of unique insights. Moreover, this technological dependency could dilute the richness of direct human interactions, weakening the fabric of social bonds. These issues—increased AI dependence, AI's encroachment on human roles, and the degradation of social ties—underscore the urgent necessity to revisit our interaction with technology, ensuring it serves to enrich rather than undermine the human experience. In light of this, our panel gathers experts to explore strategies for preserving human subjectivity through cognitive autonomy, creative agency, and social connectivity in the age of AI‐driven information practices. Our dialogue also aims to develop a comprehensive AI governance framework, scrutinized from an interdisciplinary perspective, continually refined in collaboration with academic communities, such as ASIS&T, to solidify and enhance our approaches.
Siphonophorae, an Intervention on (In)Visible Infrastructure
Authors: Bettivia Rhiannon , Stallworth Rebecca , Maurici‐Pollock Danielle , Williams Rachel
Published: October 2024

This alternative event uses siphonophorae as a metaphor for examining infrastructure in information work. Infrastructure studies are deeply rooted in the field of information science: Susan Leigh Star's work forms a cornerstone of infrastructure studies and the ways in which information science views how people operate within the systems around them (Star, 1999; Larkin, 2013). Representing a broad meta‐discipline, the ASIS&T community has decades of research and publications on infrastructure, and because the topic is so complicated and rhizomatic, we still lack clear perspectives on key questions like: Why this infrastructure, and why now? Attendees will work together to construct a physical and a digital siphonophore that constitute connections between infrastructure; those who design them; those who use them; and how they function in relation to information sciences and spaces. We seek to render infrastructure visible by asking: why this infrastructure? Doing so enables us to further interrogate: what infrastructure could be better?
Developing a Text‐Based Measure of Humility in Inquiry Using Computational Grounded Theory
Authors: Bratt Sarah , Leahey Erin , Gomez Charles , Lee Jina , Kwon Yeaeun , Lassiter Charles
Published: October 2024

We describe a project in which we develop a text‐based measure of HI in the context of scholarly communication using corpora of scientific publications. The data and analytic approach we use will circumvent known concerns with self‐reported data on humility levels and will be calculable on a large scale. We use a computational grounded theory approach to develop a text‐based measure of HI. We draw from an annotated corpus of scientific articles in economics, psychology, and sociology (2010–2023), generating three supra‐dimensions of HI (Epistemic, Rhetorical, and Transparent) and several novel sub‐codes of HI. We present our initial analysis with a focus on the three dimensions of HI derived from a computational grounded theory approach. The text‐based measure helps us better understand how contextual factors shape HI and contribute to mixed methods in information science research.
Parasitic Platforms and the Crisis in News
Authors: Brighton Jack
Published: October 2024

The viability of news has been in rapid decline since the mid‐2000s. This poster presents a critical analysis of how news publishers themselves helped precipitate the crisis by enthusiastically adopting Big Tech platform technologies and audience‐building strategies. I show how search and social media platforms disrupted publishers' relationships with audiences and advertisers by appropriating control over news distribution and revenue. I use Anthony Giddens' structuration theory and Bruno Latour's actor‐network theory to explore the restructuring of the news industry by the sociotechnical practices and surveillance economics of today's dominating platforms. Leveraging Michel Serres' concept of social parasitism, I present a research framework for assessing symbiotic and parasitic relationships in sociotechnical systems using historical, quantitative, and qualitative methods, and to identify where news publishers still have agency to begin resolving the crisis. And I suggest the urgency of this research framework as publishers rapidly adopt new AI technologies.
Doctoral Student Perspectives of Support for Seeking Mental Healthcare On‐Campus: Preliminary Findings
Authors: Carucci Damon
Published: October 2024

Even as the number of doctoral students reporting mental health challenges continues to grow, many students who experience significant mental distress do not seek treatment for these concerns. To explore this phenomenon, thirty semi‐structured interviews were conducted with doctoral students at a large U.S. public university. This poster reports on a candidate theme from a larger qualitative analysis that explored the barriers doctoral students face when seeking mental healthcare on‐campus. Insights from the theme “minimal institutional support” are presented here, and discussed with respect to health literacy, information marginalization, and consumer health information justice.
Investigating the Applications of AI in Oncology from NIH Funded Projects: Case study of Lung Cancer and Pancreatic Cancer
Authors: Cherukuri Komala Subramanyam , Chen Haihua
Published: October 2024

This poster aims to conduct an examination of the applications of Artificial Intelligence (AI) in oncology. We collect projects from National Institutes of Health (NIH) which delve into how AI applications and techniques are used in cancer research between 2018 to 2024. Results show AI's ability to enhance lung and pancreatic cancer through novel imaging techniques and data processing. We find that refined medical imaging can be done with AI‐driven techniques such as machine learning or deep learning to improve diagnostic accuracy potentially leading to better survival rates for patients involved. Additionally, this reveals how AI transformed oncology studies underlining the movement toward complex diagnostic algorithms that allow accurate stage assignments. Consequently, these forms of teamwork are essential when determining as well as making reliable Cancer care systems by developing strong AI systems with interpretation abilities using supportive NIH funding.
(Disrupting) Continuities between Eugenics and Statistics: A Critical Study of Regression Analysis
Authors: Dodson Samuel , Bartley Jane
Published: October 2024

This paper critically examines the intertwined history of statistics and eugenics through the work of Francis Galton, whose statistical inventions were guided by his problematic belief in eugenics. The paper highlights the historical development of regression analysis, arguing that acknowledging the discriminatory origins of this method is crucial for understanding historical and contemporary injustices in data‐driven decision‐making. The paper also considers the ethical implications of other statistical techniques, emphasizing the need for library and information science (LIS) students and practitioners to be aware of the societal implications of “objective” data analysis methods.
The Human‐Data Interaction Driven by Data Reuse
Authors: Duan Qingyu , Liang Mengli , Wang Xiaoguang
Published: October 2024

The data‐intensive research paradigm is sweeping through the scientific community, and new interaction challenges for interacting with data have emerged. Data reuse emerges as a critical driver of human‐data interaction, positioning data repositories at the forefront as the optimal solution for facilitating this process. This study adopts a diary study methodology to analyze the data repository‐supported human‐data interaction behaviors driven. The findings reveal that human‐data interaction extends beyond mere engagement with data, encapsulating elements of human‐computer interaction, engagement with literature, interactions with agent systems, and interpersonal communication.
Community Partnerships in Digital Cultural Heritage: A Systematic Review
Authors: Fenlon Katrina , Wise Nikki M. , Sorensen Amanda H.
Published: October 2024

Community partnerships with libraries, archives, and museums are increasingly recognized as essential to developing and stewarding complete, accurate, and equitable digital cultural records. While research and practice in this area has burgeoned, there is limited empirical research characterizing the state of the field. This study offers preliminary findings of a systematic, state‐of‐the‐art review of community‐institutional partnerships in digital cultural heritage collections and initiatives. This review systematically identifies wide‐ranging, active community partnerships among relevant institutions and their high‐level objectives, and a correlated array of approaches taken in pursuit of those objectives. The resulting conceptual framework and open access dataset form the base of a toolkit supporting community‐institutional partnerships for a more sustainable, community‐centered cultural record.
Describing Qualitative Research in ASIS&T Publications, 2018–2022
Authors: Frank Rebecca D. , Kriesberg Adam , St. Hilaire Monique
Published: October 2024

The Information Science/LIS research community welcomes research across the methodological spectrum, including qualitative methods featuring human subject research. This poster presents the research design and data collection process for an exploratory study examining qualitative research published as both long and short papers in ASIS&T Annual Meeting proceedings from 2018–2022.
Who Am I? How Public Library Staff Manage Conflicting Information about the Self‐Concept
Authors: Freeburg Darin , Klein Katie
Published: October 2024

This poster describes how public library staff manage conflicting information about their identity. Public library staff completed audio diaries about times throughout the workday that made them feel frustrated or uncomfortable, revealing times when external and internal information about their self‐concept conflicted. Their responses were coded according to established conflict styles, revealing a dynamic approach to identity construction.
Unlocking Scholarly Realms: Revealing Discipline‐Specific Publication and Citation Benefits in Open Access
Authors: Hadad Shlomit , Aharony Noa , Raban Daphne R.
Published: October 2024

This bibliometric analysis examines research publications from Israel, Austria, and Mexico (2010–2020) using Scopus data. The selection of Austria and Mexico as comparators to Israel was based on SCImago rankings. Top four disciplines were chosen for Health, Physical, and Life Sciences (HPL) and Social and Humanities Sciences (SSH). The findings indicate a distinct preference for closed‐access publications; however, a clear ‘open access citation advantage’ emerges nonetheless. Notably, Israel has the lowest SSH publications in open access, yet the highest in closed access. Conversely, open‐access HPL publications receive more citations than closed access across all countries. Despite fewer open‐access articles, Israel's HPL research surpasses Austria in citations. In SSH, Israel, and Austria's open‐access articles attract more citations, while Mexico shows no significant difference. These findings provide insights into publication dynamics, disciplinary influences, and citation patterns.
The Influence of AI Literacy on User's Trust in AI in Practical Scenarios: A Digital Divide Pilot Study
Authors: Huang Kuo‐Ting , Ball Christopher
Published: October 2024

This study explores the impact of Artificial Intelligence (AI) literacy on trust in AI across critical sectors, including transportation, healthcare, and social relationships. An online survey of 300 participants was conducted to examine trust levels in six practical AI application scenarios. The findings revealed that individuals with advanced AI literacy consistently demonstrate higher trust across all scenarios. In contrast, those with intermediate AI literacy exhibit more skepticism, particularly in high‐stakes contexts such as transportation and healthcare. This result indicates that the disparities in AI literacy can significantly shape trust levels, and the context of AI use matters. Therefore, targeted educational programs are needed to improve AI literacy, rectify misconceptions, and promote broader acceptance and trust in AI technologies. Further research should expand the demographic scope to further validate these findings and optimize educational initiatives for inclusive and equitable AI integration.
Public Perceptions of GAI: A YouTube Analysis of Sora
Authors: Huang Shilan , Goh Dion Hoe‐Lian
Published: October 2024

Text‐to‐video generative AI (GAI) tools are captivating the world with hyper‐realistic content. However, public perceptions of such technologies remain ambiguous. This paper investigates Sora, a new OpenAI offering, as an example to understand the public perceptions of GAI technology. The data comprises the 39 most popular YouTube videos about Sora and their most‐liked user comments. Through a SWOT analysis, we identify the multifaceted portrayals of Sora in the videos and the corresponding comments. Implications for future research are discussed.
Perceived Online Information Quality: An Empirical Study Using Q Methodology
Authors: Ju Boryung , VanScoy Amy
Published: October 2024

The aim of the current study is to identify the dimensions of perceived online information quality and apply Q methodology to analyze users' perceptions both qualitatively and quantitatively. Data were collected from readers and contributors of English Wikipedia through two rounds of surveys. The analysis of the first round, with 197 participants, identified 36 dimensions of online information quality, while the second round with 68 participants yielded significant five factor categories of information quality with Q methodology. Q methodology was employed to configure the design and analysis of the second round of research, especially aiming to transform users' opinions and perceptions into more solid evidence.
Tech Treasure Hunt: Promoting Children's Learning on How Algorithm Works at a Public Library
Authors: Jung Yong Ju , Liu Jiqun , Karahan Harun , Nazari Mahdieh
Published: October 2024

People in this era are surrounded by data, but still, a lot of people do not fully understand what processes occur behind the scenes such as search and recommendation algorithms trained on data of varying forms. Even though not all children will become data scientists alike, enhancing such awareness and data literacy is essential for their critical thinking and becoming active agents in the 21st century. In this regard, as part of a design‐based project, this paper demonstrates our intervention—a summer program at a public library—where children and families learned about algorithms in playful ways and shows how they made sense with the concept of algorithms.
From Fandom to Fadeaway: Unpacking Factors Contributing to Anime Dropout
Authors: Lee Wan‐Chen , Hirt Juliana , Cho Hyerim
Published: October 2024

This study explores a case of anime dropout/disengagement to understand media information users. Particularly, this study identifies elements in anime (e.g., Tropes) that prevent audience from continue watching what they had started (i.e., dropout/disengagement). A content analysis of 226 posts from MyAnimeList.net, an online anime database and fan community, suggests that the major elements that motivate audience dropout are Plot/Narrative, Tropes, Artwork/Visual Style, Characters, and Mood. The expectation gap and sunk cost also contribute to disengagement. This study presents a way to enrich anime metadata through analyzing the identified elements. Additionally, this study suggests that understanding anime disengagement may contribute to the search and recommendation of anime.
People's Voice: Exploring Discussion Themes on Generative AI and Libraries on X
Authors: Li Yuan , Sprayberry Ainsley
Published: October 2024

The launch of ChatGPT and other Generative Artificial Intelligence (GenAI) models has significantly influenced people's perceptions of AI, making its impact a widely discussed topic in various areas. In Library and Information Science (LIS), researchers experience the opportunities and challenges presented by GenAI, such as using the rich text from books and digital collections as training data for GenAI and enabling quick summarization and reference consultation for patrons. Meanwhile, concerns have arisen about the generation of fake information and nonexistent text by GenAI and their potential effects on users. This project explores the themes in public discussions regarding the impact of GenAI on libraries. To explore public opinions about the impact of GenAI in libraries, we collected over 50,000 posts from X (formerly Twitter) between November 1, 2022, and May 6, 2024. We manually sampled 1,000 posts focusing on GenAI in the context of libraries. We conducted content analysis to identify the key themes and relationships among these themes. This poster presents our initial findings on different themes and highlights the trends in public opinion. Our analysis provides a curated dataset for training APIs for future investigation.
Exploring the Curation Practices of 3D Data Creators
Authors: Lischer‐Katz Zack , Cook Matt
Published: October 2024

3D data creation methods and virtual reality (VR)‐based visualization techniques are increasingly common in interdisciplinary research and pedagogy. 3D data represent a new challenge to existing digital curation frameworks for academic libraries and other support institutions. This poster describes the research methodology, background, and research plan for an in‐progress, 3‐year project funded in part by the Institute of Museum and Library Services (IMLS grant LG‐254830‐OLS‐23). This project is exploring 3D data creation and curation practices of researchers across a range of 3D creation modalities and disciplines.
Climate Change Skeptics and the Power of Negativity
Authors: Liu Qiaoyi , Kim Yuheun , Hemsley Jeff
Published: October 2024

Climate change is a polarized topic on social media in the U.S. Actors who advocate climate change as scientific fact, or tout it as a conspiracy, both post videos on YouTube. Both kinds of videos can receive millions of views and thousands of comments. Given the polarized nature of the topic, we might expect a high degree of vitriolic speech in the comments around the videos. Previous Twitter studies would also suggest significant differences in the networks made from the interactions of such comments and replies. This study focuses on these comments and replies in an effort to understand the nature of discourse surrounding climate change believer and skeptic videos. Our hope is to extend the existing literature studying scientific communication around climate change, which to our knowledge hasn't specifically compared discussions around both climate change believers or skeptics on YouTube. Results show most users only comment on other users that align with their own perspectives about climate change, and express positive sentiment toward them. Our study also finds that the more negative the users' comment, the more connections they have with other users. These findings indicate further investigations of climate change social activities on YouTube.
Welcome To #Spoonietok: Understanding and Supporting Disability Expertise, Storytelling Abilities, and Collective Information Practices on Tiktok and Beyond
Authors: Lundy Morgan
Published: October 2024

This poster reports developing dissertation research, exploring the individual and collective storytelling practices of people experiencing difficult to diagnose chronic pain and fatigue (CSSs) on TikTok to understand and ultimately support these embodied, creative, and collective storytelling abilities on and off‐app. Ongoing data collection and analysis using constant comparison, guided by critical disability studies sensitizing concepts, of 150 videos and their top‐20 comments, and interviews with 6 community members indicate that people with CSSs are using TikTok affordances to tell and scaffold complex micro‐stories sharing disabled expertise; creatively visualize invisible illness; use intimate, documentary cinematography; tell composite stories using skits and collapsed time; and make audio, visual, and community/collective mimetic alterations. This poster shares a pragmatic sampling approach using algorithm training, and results from visualization activities during interviews which indicate a messy overlapping landscape of what I call algorithmically mediated online health communit(ies) (AMOHCs), which have slippery boundaries, where health information is found serendipitously, and people connect across specific chronic illness categories. This dissertation‐in‐progress is developing a set of disability‐specific storytelling abilities, which interview participants indicate are transferable to off‐app settings; and upcoming codesign workshops creating storytelling support materials with community members will further distill disability storytelling support needs.
Dancing in the Dark: The Case for Chaos in an Age of AI Generated Answers
Authors: McKay Dana
Published: October 2024

One of the key aims of information search tools is to organise information and make it accessible and useful, reducing the chaos of an information deluge to ten blue links, perhaps with some context about the utility of the information they represent. Generative AI answer engines, such as ChatGPT, have gone one step further, reducing the ten blue links to a single passage of text: highly digestible, easily accessible, possibly false and ultimately bland. All chaos, including disagreement between sources, possible false information, and the influence of societal biases, and the opportunity to access more information is hidden under a rug of simple, authoritative sounding text. Friction, disagreement, reflections and odd informational juxtapositions have long been known to underpin serendipity, inspiration and creativity, yet these answer engines reduce all of these opportunities, particularly in the context of complex information needs. Using ‘bounded chaos’ as a framework, we critique the trade‐offs made by answer engines, demonstrating that, even as they are non‐deterministic, they hide the wrong parts of the chaos for those who seek learning or inspiration. We make the case against bland, and defend information seekers need for perceptible heterogeneity, friction, and even a little chaos.
Heatmaps: A Tool for Studying Book Selection Preferences on Discovery Platforms
Authors: Merčun Tanja , Kuhar Maja , Jug Tjaša
Published: October 2024

This study evaluates the usefulness of heatmaps in understanding user preferences during book selection in online discovery platforms. It compares two heatmaps generated from user clicks and eye‐tracking data to identify key metadata influencing readers' choices. Initial results indicate that click heatmaps can be a useful tool for this type of investigation, especially considering the cost of eye‐tracking, which is typically used to generate attention heatmaps.
Gaining Insight: Data Needs for Firearm Injury Prevention Researchers
Authors: Million A. J. , Gidakovic Sanja , Bossaller Jenny
Published: October 2024

Between 1996 and 2018, there was little federally funded research on gun violence, even though it remains a perennial issue, resulting in considerable loss of life. Advocacy and academic groups piece together data to conduct their research, but the U.S. firearms data infrastructure is limited. A current National Institute of Health (NIH)‐funded coordinating center led by researchers at the University of Michigan aims to help confront that problem. This Quality Improvement (QI) study seeks to uncover the information needs of researchers in firearm injury prevention and community‐based interventions through interviews with public health, medical, and criminal justice researchers. Interviews reveal problems associated with existing databases and infrastructures, as well as what researchers need to support their research. We find fragmented data resources and a lag in public health data availability create obstacles to obtaining many datasets. The researchers also describe problems and opportunities for data harmonization across intervention‐focused studies.
Enabling Serendipity During Digital Library Search
Authors: Mon Ei Ei , Hoeber Orland
Published: October 2024

When a serendipitous discovery is made while searching within a digital library collection (such as an academic digital library at a university), the searcher has a difficult choice to make: either pursue the serendipitous discovery or set it aside and deal with it later. If they take the first option, this breaks the flow of the primary search activity which may make it difficult to resume. If they take the second option, they may have difficulty re‐finding what they discovered when they are finished with the primary search activity. We have developed a novel search interface that includes topic‐based workspaces and a “read it later” list. Serendipitous discoveries can be easily added to the “read it later” list, allowing the searcher to stay focused on their current search activity knowing that they can easily return to the discovered resource. For each resource saved to the “read it later” list, a textual similarity is calculated against the collection of documents saved in each of the searcher's workspaces. This allows them to easily identify which of their prior search tasks is a best fit for the discovery, as well as an ability to create a new workspace if desired.