Conference Agenda (All times are shown in Mountain Daylight Time (MDT) unless otherwise noted)

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.

Human trafficking is a complex global issue that continues to challenge efforts aimed at raising awareness and preventing its occurrence. Overall, the literature suggests that social networking platforms have the potential to significantly enhance human trafficking awareness efforts but require careful strategy development to overcome algorithmic limitations and ensure meaningful engagement with users. Further research is needed to evaluate the long-term impact of social networking campaigns on human trafficking awareness and prevention. This poster is an extension of a presentation at the ASIS&T Midwest Chapter Spring 2024 Symposium with the purpose of increasing attention to this important research area. This poster provides five broad research questions that can be immediately addressed by information science researchers for the purpose of increasing understanding of this devastating global issue.

This study investigates disparities in telemedicine access and use among racial groups in the United States during the COVID-19 pandemic. Our analysis of a dataset from the National Center for Health Statistics (NCHS) reveals that telemedicine access and use varied significantly across racial groups, which the NCHS categorized as “Black,” “Hispanic,” “White,” or “Other.” Hispanic respondents were more likely to lack a regular place of care, which might explain their lower access to telemedicine, relative to other groups. White respondents reported relatively high telemedicine access. Black respondents also reported relatively high rates of telemedicine use, contrary to the literature suggesting lower healthcare access among African Americans. Findings underscore the need for public health agencies to disseminate information about telemedicine to all community members. We suggest future research exploring the socioeconomic barriers impacting individuals’ ability to access and use telemedicine.

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.

While participating in the information search process, students are instructed to choose credible sources for their research. They often interpret this imperative as simply avoiding bias in the sources they choose. Unfortunately, students misunderstand bias and fail to recognize its significance in the scholarly conversation. One way librarians can help with this misunderstanding is by engaging with theoretical concepts such as positionality theory. Positionality theory offers librarians a way to examine bias’s function in the research process in order to teach students about its complexity.

While bias and positionality are concepts that have been explored in information literacy research, most research does not address them as related topics. This poster explores notions of bias, positionality theory, and strategies to employ that will help librarians use this theory to address student misunderstandings around questions of bias and credibility. This new understanding helps students determine their own positionality when approaching information searching.

In this paper we examine the determinants (trust and social influence) that influence users’ future use intentions of generative AI applications among black, indigenous and other people of color (BIPOC) users of generative AI applications such as ChatGPT and other large language models. An online survey instrument was administered to 119 BIPOC identified individuals residing in the United States. Descriptive and inferential statistics were used to analyze data through SPSS. Results indicate no statistically significant differences among ethnic/ racial groups in social influence and continued use intentions. However, with respect to trust, Hispanic/LatinX users perceive information from AI chatbots as more trustworthy and accurate, and that their search activity was more secure than other BIPOC populations. The study also found that trust and social influence were significant factors driving future use intentions.

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.

As part of a larger project to develop a scale for measuring web credibility perceptions, this poster reports on preliminary findings of a literature analysis to identify reflective and formative indicators of information credibility on social media. Of 90 papers found by a systematic database search and screening, this poster examines 20 papers focused on information credibility on microblogs (e.g., Twitter, Weibo) as a popular source for online users’ information seeking. Our analysis identified 22 reflective indicators (e.g., fair, accurate) and 31 formative indicators (e.g., reputable, attractive), eight (e.g., trustworthy, unbiased) of which overlapped. Given the intertwined, often blurred border between these two sets of items, the coding scheme developed in the present study can help determine the relevance and type of the items. Future research directions are discussed.

Affordances, described as action opportunities emerging from the relationship between a technology and its user, are a popular framework for understanding how people engage with social media platforms. Scholars have proposed this framework as a tool for performing critical analyses of technology, arguing that drilling down on affordances can help us understand how power shapes technology use. This conceptual analysis builds on the affordance mechanisms and conditions framework by Davis. Davis proposed to think of affordances in terms of mechanisms (request, demand, encourage, discourage, refuse, allow) and conditions (perception, dexterity, and cultural and institutional legitimacy). This framework sheds light on to whom and under what circumstances technologies afford certain actions to its users. I apply this framework to social media and provide examples that illustrate the framework’s components. For each component, I include a set of suggested questions that a researcher interested in a critical social justice-oriented analysis of social media affordances might ask. Asking these questions, I argue, can support a researcher in dissecting the biases, stigmata, and discrimination embedded in social media.

This study explores the effects of diversity, equity, and inclusion work on retention and job satisfaction of librarians. Using existing data from the Workforce Issues in Library and Information Science 2 project, a moderated mediation SEM model showed that being prepared to advocate for or serve diverse populations did not directly affect librarian retention, nor did collaborating with the community or promoting outreach and engagement with the community. However, this preparation and these job activities did affect librarians’ job satisfaction, which ultimately affected their decision to stay in the profession. In addition, preparation for DEI work and opportunities to work with the community are just as important for White librarians as for Black, Indigenous, and People of Color (BIPOC) librarians. These findings have implications for library and information science education, practice, and research.

This paper introduces TORCHLITE, an innovative HathiTrust Research Center (HTRC) open analytical and computational framework designed to offer efficient, open, and approachable access to the HTRC Extracted Features (EF) dataset via a well-documented web-based API. This poster will summarize project goals and progress, and discuss community engagement, which has played a pivotal role in this project. A hackathon event held in spring 2024, TORCHLITE fostered collaboration among digital humanities and information science scholars to develop widgets and notebooks utilizing the EF API. Through the hackathon, participants explored the API's capabilities, leading to the creation of over a dozen analytical widgets and interactive programming notebooks (e.g., Jupyter).

Library and information science has led data curation research and education for the last two decades, providing data curation education, professional development programs, and a robust professional opportunity. To keep current with the latest trends in competencies and tools needed to conduct data curation activities, this research conducted a systematic literature review of literature that captures competencies and tools to build a framework of current trends in data curation work that educators can utilize to ensure up-to-date educational materials for the next generation of data curation professionals. This poster presents the preliminary findings of this data curation competencies and tools analysis.

As AI-enhanced academic search systems become increasingly popular among researchers, investigating their AI transparency is crucial to ensure trust in the search outcomes, as well as the reliability and integrity of scholarly work. This study employs a qualitative content analysis approach to examine the websites of a sample of 10 AI-enhanced academic search systems identified through university library guides. The assessed level of transparency varies across these systems: five provide detailed information about their mechanisms, three offer partial information, and two provide little to no information. These findings indicate that the academic community is recommending and using tools with opaque functionalities, raising concerns about research integrity, including issues of reproducibility and researcher responsibility.

Many scholars have tried to investigate the identification, characterization, dissemination, and prevention of misinformation in recent years. But a fundamental question that lies behind these investigations is ‘Why do people believe in a piece of information, whether true or false?’. The primary objective of this study is to understand the psychological drivers of belief systems that makes individuals believe any information prior assessing its veracity. The study specifically tries to understand cognitive biases that influence an individual’s decision making about information in digital settings. Based on a quantitative survey with 41 participants, we try to induce cognitivity among the participants and try to measure the effect in their decision making. We find a major portion of our participants being cognitively induced which in turn had a significant effect on their decision making while engaging with information. Furthermore, we try to assess whether an individual’s political affiliation has any effects in perceived truthfulness while engaging with political information. This study shows us how easily cognitive bias can be induced and how it affects an individual’s belief structure in digital platforms.

This study aimed to construct a model of memory institutions (libraries, archives, and museums) as a lens for analyzing contemporary collective memory practices related to the Ainu people. First, we developed a pilot model to organize the current phenomena and practices of collective memories and suggest a respective circulation mechanism consisting of three core elements: media, collective representations, and collective memory. Additionally, employing this pilot model, we conducted close readings of 449 pieces of literature and illustrated the characteristics of collective memory practices and phenomena in a case study of the Ainu. Our findings indicate that 1) the traditional collective memory of the Ainu has gradually shifted toward recognizing them as a unique ethnic group, and 2) by translating their oral literature into diverse media, the Ainu have reshaped their cultural identity.

This paper deconstructs the profound context of Qi Culture from the perspective of Meme and constructs a structured and systematic knowledge description framework. It is useful for exploring the core values and promoting the creative transformation and innovative development of Qi Culture. The research process involves collecting user discussions and research data about Qi Culture from multiple sources, and then using the BERTopic model and qualitative analysis methods to obtain diverse examples of Qi cultural elements. Afterwards, from the Meme perspective, Qi cultural memes are extracted to construct a domain ontology, thereby achieving multi-dimensional semantic knowledge description of Qi Culture. This study helps to reveal the core elements of culture and their relationships, and will provide strong theoretical support for the integration and utilization of cultural resources.

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 during the so-called Web 2.0 era. I show how search and social media platforms disrupted publishers’ relationships with audiences and advertisers by appropriating control over news distribution and revenue. Using Anthony Giddens’ structuration theory, I explore the role of the platforms in restructuring the practices of newsrooms and undermining the values of journalism. I present a research framework for assessing symbiotic and parasitic relationships in sociotechnical systems through a mix of quantitative and qualitative methods. I use Bruno Latour’s actor-network theory to identify technological power in relationships between news publishers and Big Tech, and to suggest where publishers still have agency to begin resolving the crisis.

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.

Digital housekeeping (DH) is an emerging research topic focused on the labour and social implications of setting up and maintaining technology in the home. Although examined by multiple fields, the literature on DH has yet to be reviewed and its potential overlap and synergy with library and information science (LIS) have yet to be explored. We therefore conducted a tool-supported review of 26 scholarly publications on DH. Below we summarise the people- and relationship-focused sub-topics and findings of DH literature and explore promising directions for future research in LIS, particularly in information behaviour and personal information management. The poster visually maps the sub-topics and disciplinary connections.

When interacting with chatbots, questioning is the starting point of the conversations. A well-phrased question can elicit desired responses and enhance information-seeking efficiency. Specifically, consumers can use various linguistic cues (e.g., emotional expressiveness) to phrase questions and shape responses. However, research on how to effectively phrase questions for chatbots was limited. This study focuses on consumers' questions for chatbots, examining how various linguistic cues (e.g., emotional expressiveness) influence responses. Further, it compares responses generated by community users and chatbots. Preliminary results showed that askers’ self-disclosure attracted emotional support from chatbots. Chatbots demonstrated higher expertise in responses to high-specificity questions and produced more analytical and unified responses. In contrast, user-generated responses were perceived with higher authenticity. This research underscores the role of question cues prompting chatbot-generated responses and illustrates the strengths and limits of chatbot-generated responses. Theoretical and practical implications are discussed in the final section.

This poster presents a community-focused participatory design methodology as a basis for developing a digital storytelling system for the Inuvialuit Settlement Region in Canada’s Western Arctic. It provides an overview of the various stages of digital storytelling system prototype design informed by an Inuvialuit elder’s storytelling gathering and a community experience open house. Among the prototype redesign ideas are empathy maps we are using to allow us to connect stories with designs and community needs in mind.

Open government information systems offer great potential for advancing civic life and democracy, but they also reflect and reinforce the biases and systematic inequalities faced by members of socially marginalized groups. We present results from a critical data modeling project that uses a data quality framework to examine open datasets published by police departments in order to understand how data modeling choices shape the social impact of these datasets. Using an arrest record dataset published by the Los Angeles Police Department as a case study, we present we present results detailing the representation of racial data and the presence of children in the dataset. We argue that current data quality frameworks for open government data are insufficient for critical data studies due to an orientation around institutional and computational interests. Incorporating feminist data ethics into data quality analysis provides an approach to data quality that centers people and communities. We propose a definition for data quality of open government datasets based on an ethics of care that centers the needs of vulnerable populations and accountability of institutions toward their communities.

Research into multimodal model biases has largely concentrated on gender and racial issues, neglecting key areas such as biases related to religion, nationality, sexual orientation, and disabilities, as well as the subtleties of question intent and common biases. To address these gaps, we developed the Multimodal Bi-Stream Structure-based Model (MBSS) for Bias Mitigation, an innovative approach that focuses on these underrepresented groups by focusing on religion, nationality, sexual orientation, and disability but also to identify common biases and question intents. The MBSS employs a dual stream bias mitigation system, where the standard stream addresses common biases and the mitigation stream targets specific biases, allowing for nuanced and effective bias reduction by making decisions based on the difference in predicted probabilities between these two streams. Empirical results demonstrate MBSS's efficacy, especially in reducing religious bias by up to 71%, highlighting its potential to foster a more inclusive digital environment and enhance the representation of diverse groups.

Although the Social Q&A community has gained increasing popularity in solving issues in various fields, how users perceive the information credibility on this platform remains inconclusive. This paper developed the model to discuss the relationship between linguistic cues/perceived reputation and information credibility. Methodologically, the paper used an online survey to collect 759 units of valid data and test the model through the SEM technique. The results showed that perceived specificity, logic, relevance, and reputation are positively related to information credibility, while perceived expressed emotion showed a negative association. These findings help to inspire answer contributors to employ strategies to make their answers more credible.

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.

This poster describes research considering how public library staff manage conflicting internal and external information about their identity. 21 public library staff members completed audio diaries and sat for an interview 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.

As competition in technologies such as semiconductors and AI intensifies, the importance of technology analysis in establishing R&D strategies is increasing. Although frequently used, technological document searches by domain experts often suffer from limitations in qualitative judgment and inconsistency. This study proposes a process for quantitatively assembling a collection of documents on specific technologies using an academic database and a language model. Using approximately 92 million records from the Web of Science, we identified related keywords from author keywords and used scientometric approaches to determine a core set of technology-related literature. Then, a deep learning-based Sentence Transformer model was employed to extract technology literature with high similarity to the documents, ultimately forming a set of documents for technology analysis. This study aims to overcome cognitive limitations and introduce quantitative criteria for technology analysis, which is particularly significant for data-driven science and technology policy.

Universities now focus on the societal impact of research, yet there is a disconnect between the information environment and the needs of academics undertaking impact-related work. While universities acknowledge that undertaking research designed for societal impact requires changes to research culture and practices, the information and support provided do not reflect the complexity of that work. Professional staff mediate information from various sources, such as government policies, university strategies, funders’ requirements, and academics’ needs; however, the effectiveness and appropriateness of support provided is understudied. This qualitative study explores the information behaviors of professional staff developing and facilitating academics’ engagement in impact work. It finds that while professional staff undertook iterative processes of self-informing, mediation, and information use to support impact work, their privileging of institutional and external influences, ahead of academics’ needs, creates a disconnect between services provided and what academics need. This study increases awareness of the influence of professional staff’s information behaviors on impact supports, which can inform academic-centered, responsive approaches to support contextualized, personalized and responsive information services in universities.

The development of cultural and creative products by libraries helps to promote the creative transformation and innovative development of culture, and meets the demand for high-quality cultural consumption. Based on the formation mechanism of library cultural and creative product consumption behavior, this study takes China as the research object and proposes that libraries should pay attention to the evolution of the consumption theme, meet the symbolic consumption demand, create new consumption scenarios, pay attention to product quality improvement, and improve the awareness of consumption service. The results of the research show that in the product dimension, the richness of categories is emphasized, and the aesthetic attributes of products are emphasized. In the demand dimension, it focuses on commemorative value and social value, and satisfies both emotional value and cognitive value. In terms of channel dimension, it covers both online and offline channels, and reasonably utilizes the KOL (Key Opinion Leader) effect. In the price dimension, we refuse to spend a high amount of money, but are willing to pay for quality and originality. In the service dimension, focusing on the purchase experience, the service quality affects the subsequent consumption.

Hundreds of Libraries, Archives, and Museums (LAMs) around the world run crowdsourced transcription projects in order to engage users with their collections. Some LAMs explicitly use crowdsourcing projects to make non-machine-readable images of documents, such as manuscripts, discoverable to people who are blind or have low vision. We present findings from Crowdsourced Data: Accuracy, Accessibility, Authority (CDAAA), a 3-year Institute of Museum and Library Services (IMLS) grant project that investigates whether and how LAMs integrate crowdsourced transcriptions into their discovery systems, and whether these efforts result in accessible web-content for blind people and those with low-vision who use assistive technology to navigate the web. We share research findings as well as practical suggestions for those in charge of crowdsourcing projects, the resulting transcription data, or similar web-based textual content such as scholarly editions. These research and practice-oriented findings are relevant to any national or local context where inaccessible images are transcribed, and are especially timely in the US context given recent Federal rule-making to ensure that all web and app-based content provided by US State and local governments is accessible, including tools, resources, and content created in-house, through contracts or by license (2024).

Young adults have been shown to prefer online sources when searching for information about sensitive topics, such as sexual health (Baker et al., 2020; Buhi et al, 2009). This is especially true for LGBTQ+ young adults, who may want to keep potentially sensitive information needs about their sexuality or gender identity a secret as they explore their identity, or because they may need to for safety reasons (Delmonaco & Haimson, 2022; Kitzie, 2019). This pilot survey study aimed to explore what types of information needs LGBTQ+ young adults may keep secret from the people in their lives, at what age they had their first secret information need, what skills helped them to fulfill their secret information needs, and which skills/resources they would have liked to have when trying to address their secret information needs. Preliminary results suggest that topics relating to health and participants’ LGBTQ+ identities were more likely to be kept secret than other everyday life information needs.

Cybersecurity is a perennial concern for technology companies, consumers, and policymakers. Recently it has become a lever of opposition against the Right to Repair movement as companies and industry groups position security and reparability as antagonistic, even incompatible, interests. This paper pushes back on those claims. We use critical discourse analysis to explore two contemporary controversies at the intersection of security and repair: (1) the U.S. National Highway Transportation Safety Administration's statements concerning automotive repair in Massachusetts and (2) the memorandum of understanding between John Deere and the American Farm Bureau Federation concerning agricultural equipment repair. We find that security concerns are raised as featureless specters of harm rarely supported by concrete or compelling evidence that reparability risks security. Rather, these arguments reflect a rhetorical strategy aimed at thwarting the repair activities of consumers and independent technicians, shifting ongoing policy debates, and influencing public sentiment around the right to repair.

This systematic literature review synthesizes published sources from the ASIS&T Digital Library and the ACM Digital Library to develop a definition of the carceral state and to show how the term has been used in contemporary technology-focused research. The carceral state concept has been adopted and applied widely in multiple areas of social scientific research to refer to the formal institutions of the criminal justice system proper and other social arrangements, ideologies, practices, and technologies that punish, surveil, and contain populations. Our review reveals a recent and increasing engagement with the carceral state in the collections surveyed. Encouraged by this increasing attention, this review is an attempt to introduce the carceral state as a guiding framework for tech-society research and to consider implications for advancing responsibility, reflexivity, and care in the creation and evaluation of information systems, programs, and services.

This research takes the accessibility of rural public digital cultural services as the research object. Based on the accessibility framework and system evaluation theory, an evaluation index system is constructed through investigation and analysis. Firstly, through on-site investigation and interviews, explore service accessibility factors and establish a feature factor model; Secondly, based on industry norms, an evaluation system was constructed that includes primary, secondary, and tertiary indicators, and was revised and validated through empirical analysis; Finally, the AHP- fuzzy comprehensive evaluation method was applied to study the indicator system. The results indicate that the accessibility of public digital cultural services is good, but still needs to be improved, and improvement path suggestions are proposed from the aspects of resource supply, services, organization and management, etc. The research enriches the evaluation system of public cultural services and provide reference for the government to improve public digital cultural services.

This exploratory study investigates the influence of stressful life events on privacy-protective behavior. We examine the effects of the ongoing state of war in Israel on the rate of deliberate falsification of personal details during website registration, in a user study of 107 students at Israeli universities. The students were queried using a quantitative method in online closed-ended questionnaires. This research builds on our previous work (Weinberger & Bouhnik, 2021) examining the effects of other stressful life events, specifically the COVID-19 pandemic, on the privacy-protective behavior of Israeli students. We found that distrust of website operators and the desire to remain anonymous are the most common reasons for identity falsification. In addition, our analysis showed that the greater the concern over privacy, the more likely identity falsification was to occur. The Israel-Hamas War in Israel seems to have exacerbated privacy concerns among online users, in turn increasing the tendency of users to exhibit privacy-protective behavior.

This study investigates the impact of ancient book image quality on the accuracy of cultural heritage crowdsourcing correction tasks. The study processed 27 pictures from an ancient book to adjust their quality in terms of clarity, seal coverage and background color. Based on human error theory, the study divided common errors made by participants in correction tasks into slips and mistakes, and analyzed the impact of different image qualities on these errors. The experiment was conducted using an online experiment platform, with 25 participants each completing 6 tasks related to clarity, seal coverage, and background color. The results show that the clarity of ancient book images significantly affects the number of rectifications and mistakes in the tasks. Different degrees of seal coverage significantly impact the number of rectifications and slips, while the background color of the images mainly affects the number of mistakes.

Preprints have played an unprecedented role in disseminating COVID-19-related science results to the public. The study aims to elucidate the role of preprints during the COVID-19 public health emergency (2020-2023) through a quantitative analysis of journal papers. Among the 247,854 COVID-19-related papers published in PubMed, 12,152 were initially released as preprints and were eventually published in 1,380 journals. This number is more than five times the 246 journals to which submissions can be made directly from bioRxiv through the B2J program. Journals with higher impact factors and Normalized Eigenfactor scores tend to publish a larger number of preprint-derived articles. The proportion of preprints among PubMed papers was 0.049, but this varies significantly by journal. In the top 30 journals, most exceed this proportion, indicating that these journals are preferred by authors for submitting their work. These findings highlight the growing acceptance and impact of preprints in the scientific community, particularly in high-impact journals.

This study explores the influence of Artificial Intelligence (AI) literacy on trust in AI across critical sectors, including transportation, healthcare, and social relationships. An online survey was conducted with 300 participants. The findings revealed that those with advanced AI literacy were more likely to report increased trust across all scenarios. However, those with intermediate AI literacy seemed to view AI with more skepticism, particularly in high-stakes contexts such as transportation and healthcare. This result indicates that AI literacy is crucial in shaping public attitudes toward AI technologies, 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 validate these findings and optimize educational initiatives for equitable AI integration.

Drawing on knowledge creation capability in the Science of Science, this paper aims to determine scientists` knowledge creation capability (KCC) and figure out whether there is any gender gap in that. We construct a career matrix to compute KCC of scientists in Economics, Astronomy, and Computer Science each year, from the perspective of knowledge source and diffusion. We find that male scientists` KCC is not significantly different from that of female scientists, and the two groups of scientists have the same KCC distribution. In the first 10 years of their academic careers, female scientists have higher KCC than male scientists but have lower KCC in the 15 to 40 years. After controlling a series of confounders, it`s found that female scientists have higher KCC than male scientists, even though there is not much of a difference in KCC. The results remain the same after we perform a robustness check.

Little is known about the differences between trolling by influencers and followers, the difference between proactive and reactive trolling, and the relationship between them. Based on a content analysis of 1,386 comments on 160 posts of 19 influencers during a Sina Weibo trolling event, we found that influencers troll more frequently than their followers and they troll proactively more often than reactively, while followers troll reactively more often than proactively. Influencers generated less but more impactful content compared with their followers. In both proactive and reactive trolling, influencers derailed the discussion while followers provoked through their proactive trolling and insulted through their reactive trolling. These findings extend the research scope of trolling asymmetry and establish theoretical connections between social media roles and proactive-reactive trolling.

It is of paramount importance to support midwifery practice with research evidence. Nevertheless, Canadian midwives encounter significant obstacles in accessing research evidence for practice. As part of a broader study which seeks to identify an optimal method for providing midwives in Canada with access to online clinical resources, this poster reports on the preliminary findings of the survey phase. The objective of this study was to ascertain the frequency with which Canadian midwives utilize information sources accessible to them through institutional affiliations and to determine whether they are aware of the financial cost of providing such services. Additionally, the survey sought to ascertain whether their perception of a resource being free or premium affects its perceived value and use intentions. A descriptive survey design employing a structured web-based questionnaire was used to collect the requisite data. The preliminary findings indicated that participants perceived the value of free sources to be lower than premium sources and that they intended to use premium sources more frequently if they were able to pay for them. Moreover, the study demonstrated that participants frequently seek information online to support clinical practice, yet they utilize access to online resources through their institutional affiliation less so.

A key challenge in conducting comparative analyses across social units, such as ethnicities, cultures, or religions, is that data on these units is often encoded in distinct and incompatible formats across diverse datasets. This can involve simple differences in the variables and values used to encode these units (e.g., Roman Catholic is V130 = 1 vs. Q98A = 2 in two different datasets) or differences in the resolutions at which units are encoded (Maya vs. Kaqchikel Maya). These disparate encodings can create substantial challenges for the efficiency and transparency of data syntheses across diverse datasets. We introduce a user-friendly set of tools to help users translate four kinds of categories (religion, ethnicity, language, and subdistrict) across multiple, external datasets. We outline the platform’s key functions and current progress, as well as long-range goals for the platform.

Archives can reinforce silences of marginalized communities, further erasing their stories and voices. This study aims to understand the contours of archival silences, focusing on experiences of Chinese people in the United States in the late 19th and early 20th centuries. We are building a digital archival collection that centers Chinese peoples’ voices around the Rock Springs Massacre of 1885, an incident of racial violence that killed at least 28 Chinese people and displaced hundreds more. As we selected items, we wrote structured reflections to critically examine each item’s content, source, presentation, arrangement and description in their original context, as well as any absences and silences that we encountered in the search and assessment process. Based on a praxis of radical empathy, ethics of care, and slow archives, we engaged with our positionalities, past experiences, professional training in Library and Information Sciences (LIS), and affective responses throughout the research process to reflect on how they shape our decisions and hesitations in item selection and contextualization. Three main themes emerged from a preliminary thematic analysis of our reflections: complexity of archival silences and silencing, challenges of inclusive representation, and problematizing and going beyond primary sources.

Critical Race Theory (CRT) has recently garnered considerable political attention. Given the increasing role of white identity in politics, CRT discourse provides an opportune moment to leverage white Americans’ growing grievances. This paper examined how partisan news headlines incorporated white in-group identity in the discourse surrounding CRT and how these narratives spread within partisan media ecosystems. We employed a critical disinformation approach and computational methods to analyze partisan news headlines (N = 7,320) shared between January 1, 2016, and December 31, 2022. To investigate how the narratives spread within partisan news media ecosystems, this study adopted granger causality tests as a method of time series analysis and the study results disclosed evidence of intra-lean influence among right-leaning sources.

Based on the Governing Knowledge Commons framework, this study examines what users do and think about managing misinformation issues in the Twitter community, and how these are affected by user characteristics. Undergraduates from a public university in the US participated in the study (n = 464) by completing an online survey. Findings shed light on how Twitter community members tackle misinformation issues and inform policies and system designs to support the members’ actions and preferences.

Licensing data is critical to promote research data sharing and reuse. There are many standard licenses, customized licenses, terms of use, and use agreements used in practice, which are heterogeneous and hard for data providers to choose. This study used a qualitatively-driven-mixed method, including stratified and systematic sampling methods, and Grounded Theory to explore data licensing of the Dataverse repositories. We focused on the data usage and created a list of categories of permitted and restricted data usage. The categories help data providers understand and choose appropriate licenses when sharing data. They also build a foundation for our future work to create a faceted knowledge structure for research data licensing.

This poster explores the impact of design in historical and cultural games, focusing specifically on JX3 Online, a Chinese 3D massively multiplayer online role-playing game. It investigates how these designs influence players’ perceived value, game satisfaction and game engagement. The study leverages data from an online survey of 300 respondents. It finds that both aesthetic and functional design elements, along with accurate historical representation, significantly enhance players’ perceived value. Additionally, a positive relationship exists between players’ perceived value, satisfaction, and engagement levels. This research underscores the potential of historical and cultural games for cultural education and dissemination. It emphasizes the need to blend engaging design with educational content effectively.

Remote work and its challenges have been around for decades, yet the COVID19 pandemic brought these challenges to the fore and prompted organisations to address them. One of the challenges is the potential loss of tacit knowledge (knowledge that resides in the heads of “knowers”) among knowledge workers by shifting from in-person to remote work. This paper reports on the initial findings of a pilot study that interviewed knowledge workers about the interruption of information sharing, the importance of technology to resemble in-person working, and organization adaptability. The findings highlight the importance of in-person interactions to exchanging tacit information but indicates that for organisations that are resilient, technology can mimic in-person sharing.

Small and Medium Enterprises in rural areas are likely to be impacted by the digital divide arising from their geographic location and size of their organizations. With the integration of AI in to many business processes and systems, it is important to investigate how AI will impact the digital divides, especially in light of its potential to minimalize this disadvantage. This paper presents the preliminary findings of a qualitative study investigating how SMEs in rural Norrland are impacted by AI.

The information behaviors of people who have COVID-19 have been well studied, however, there is a gap regarding those who experience long COVID. We conducted an exploratory mixed-methods study to examine the information needs and practices of these COVID ‘long-haulers’ with the intention of suggesting new health communication strategies to help people in similar health crises or future pandemics from an information standpoint. Several themes have emerged from our preliminary findings: how participants tend to define long COVID, their long COVID-related information seeking and their difficulty with finding such information, and their experiences with discrimination and stigma. Throughout data collection, we have experienced issues with suspicious respondents (such as potential bots), though we were able to implement new criteria to determine legitimacy. Data collection and analysis is still ongoing and we expect to report additional findings in the near future.

Toxic content on social media has posed a significant threat to user experience and societal stability. In contrast to explicitly toxic content, implicitly toxic content, lacking overt toxic words, is challenging to identify through single-text features. Therefore, this study proposes a multi-feature fusion algorithm to recognize implicit toxic content. Firstly, we collect various features for each post, including text content, likes, comments, user information and other features. Subsequently, employing a multi-head attention mechanism, we extract and fuse these features. Then, utilizing ensemble learning algorithms, we identify implicitly toxic content based on the fused features. Finally, we analyze the decision-making process of the model using an interpretable algorithm and derive the most critical features and factors for identifying implicitly toxic content. The experimental results show that the proposed algorithm outperforms algorithms such as BERT, TextCNN, and XGBoost, demonstrating the advantage of multiple features over a single text feature in recognizing implicitly toxic content. In addition, this study provides insights into the decision-making process of the model and provides more effective toxic content management strategies for social media platforms.

The rapid growth of artificial intelligence (AI) and machine learning (ML) has led to significant innovations but also raised ethical concerns. Researchers and students designed an ethical online game in this study to spread awareness about making informed decisions when using AI and ML. Conducted within a directed research group (DRG) curricular method, the study engages students as co-researchers to develop a game, from developing ideas to playtesting the game in a class setting. The study employs a quantitative methodology to analyze a survey that 32 students, each with diverse backgrounds and knowledge in game development, conducted after each class session over three semesters. Findings indicate that self-reported engagement changes depending on the activities done in each session, with students feeling capable of contributing to research and game design.

Towards the goal of constructing an agent that can assist users in completing complex tasks and searching for information from multiple sources. We propose a complex task-supporting application, SMART (Search Maps and Routes/Trails), which combines complex task modeling through users’ web activities and expands search support to include maps and routes/trails of both subtasks and sources within complex tasks through case-based reasoning. We also summarize the results of a user study conducted to evaluate the usability of the SMART prototype system. The experimental results show that SMART could help searchers make search strategies, select information sources effectively, increase confidence during task performance, and improve search results. This highlights the value of both types of maps and routes/trails of subtasks and sources in complex task-supporting.

Cataloging and metadata professionals in libraries have an interest in learning about the Semantic Web and Linked Data technologies. However, with limited release time and funding for continuing education, professional development opportunities need to be designed with learner needs in mind: free, flexible, and fun. Our regional Linked Data interest group approached this challenge by developing an annual program in the form of a five-day edit-a-thon, providing training and hands-on experience in creating and editing Wikidata. The flexible format makes use of synchronous video conferencing, detailed online documentation, independent editing days for hands-on learning, and opt-in community discussion via Slack. Individual and project progress was tracked with the Wikimedia Event Dashboard. This poster proposes the Wikidata edit-a-thon as an ideal method to fulfill a professional development need of library staff by introducing participants to the principles of Linked Data through this flexible and interactive event.