Conference Agenda (All times are shown in Greenwich Mean Time (GMT) unless otherwise noted)

This poster proposes a conceptual model to understand how digital events promote intangible cultural heritage (ICH) from a user experience perspective. The model is tested using survey data from 149 valid respondents. Our results indicate that two important attributes of digital events (event design and historical re-enactment) significantly contribute to users’ positive perception of ICH promotions. This positive perception further leads to increased attraction, user satisfaction, and engagement with ICH promotions. The value perception of ICH promotions includes three components: perceived utilitarian value, perceived hedonic value, and perceived symbolic value. We also discuss the theoretical and practical implications of our findings.

The decision-making process for potential college students in the United States is overwhelming because of the volume of information available and the complexity of higher education systems. Prospective students must consider institutional rankings, academic programs, and financial aid opportunities in addition to completing the numerous forms. For some students, there exist personal and professional resources to assist with navigating the process. Others with less social and cultural capital must figure out the system with little or no assistance while facing other barriers. Public libraries can be a resource for the college-bound community; however, research shows that public libraries are not actively engaged in supporting this user group. To provide focused, quality information services to any demographic, it is important to understand the perspectives of frontline information workers. Thus, through an online survey, this study seeks to learn staff perspectives on the role of public libraries in serving prospective college students.

In the rapid flow of digital transformation, kiosks have naturally become part of our daily lives. In this study, we targeted visually impaired college students in their 20s, the primary user group of kiosks. We evaluated the usability of a self-service certificate issuance and fast-food restaurant kiosks. Based on Nielsen's five usability evaluation criteria, we presented experimental tasks to the visually impaired college students, monitored their performance, and conducted interviews to assess usability. Through this process, we aimed to understand the usage difficulties of visually impaired people when using kiosks and identify their specific requirements. Furthermore, we aimed to provide insights into improving the accessibility and usability of kiosks for this population and offer practical implications for developing kiosk education programs.

This work-in-progress study explores users' perceived credibility of health-related YouTube videos and proposes a credibility evaluation framework consisting of twelve criteria across four levels: source, content, creator, and interaction. College students who use YouTube for health information were invited to participate in an online survey. A pilot study was carried out with a small sample size. The findings from the pilot study showed that participants frequently accessed health videos, mainly for specific diseases, treatments, mental health, nutrition, and fitness. There were statistically significant associations between source and interaction, as well as source and creator. Further analyses with a larger sample size will be performed and reported at the 2023 ASIS&T annual meeting. We believe the findings from this study could enhance the understanding of users' attitudes and behaviors of seeking and sharing health information on YouTube.

This paper presents an overview of coverage of OpenAI’s ChatGPT in media outlets from November 2022-March 2023, a comparison to previous media coverage of the chatbot Tay across the same outlets, and a count of ChatGPT media articles pertaining to government legislation and regulation. The New York Times, Wired, Gizmodo, The Globe and Mail, and The Guardian were searched for coverage. Across all five outlets there is an uptick in media coverage surrounding ChatGPT, with total numbers of included articles per month being 0 in November, 39 in December, 68 in January, 104 in February, and 143 in March. Findings exemplify the trend of increased coverage of ChatGPT in media public discourse, which contrasts with previous smaller media coverage of Tay. Examination of headlines and subheadings of included articles reveals minimal coverage (0.057%) dedicated to government legislation of ChatGPT. Future research will evaluate what is being said about ChatGPT within these media outlets.

This study reviewed qualitative studies that focused on library practices for supporting early literacy development. A meta-ethnography approach was used to locate, evaluate, and synthesize the findings of these studies. Based on a search in the Web of Science and a nationwide database developed by the National Central Library, 16 articles were included in the synthesis. Drawing upon Bronfenbrenner’s ecological systems theory, the findings identified five key themes (i.e., diverse collections, innovative activities, proactive intermediary role of librarians, print-full environment, and community partnership) related to library practices that support early literacy. By interpreting the findings of the review, suggestions and implications were discussed for library services supporting early literacy.

Although open government information awareness has enhanced since China implemented the Open Government Information Regulation, the complaints about the information disclosure work also increased. Previous research has ignored the impact of the department’s responsibilities on information disclosure, which are essential as some departments handle information that should not be disclosed. From this standpoint, this paper is the first to analyze the differences in departments’ open government information performance from their annual information openness reports. Variance and correlation analysis show that the potential harm of disclosure to third parties’ legitimate rights and interests, the availability of relevant information, and duplicate requests can explain their differences in government information disclosure on request. Besides, this paper calls for an elastic criterion for openness evaluation and finds that the benchmark effect in previous research does not apply to departments with different responsibilities.

Vicarious learning (VL), a concept widely explored in organizational behavior, has also received attention in social media research in recent years. Compared to live streaming e-commerce and online communities, less VL has been studied based on danmaku, an instant commentary in video sites. We consider the characteristics of human information interaction in danmaku contexts and focus on exploring the core elements of coactive vicarious learning (CVL). Five core elements are identified: experience sharing, analysis & explaining, emotional support, controversy & debate, and content extension. We will further explore the influencing factors through content analysis and semi-structured interviews.

There are many valuable insights on jobs and professions in different sectors of society based on their imminent and ascribed characteristics. Studying such characteristics traditionally was done by action research, surveys, questionnaires, etc. which typically take much time and resources to be concluded. In this study we examine vocational education and training data on Twitter. While we present a generic framework to retrieve, process and analyze tweets, we will discuss two research questions from computational social science: First, how can we make Twitter data interoperable to other available resources, e.g. classifications of occupations, tools and skills? Second, do we have enough data to process job collocational prestige analysis on a geographical basis? This presents a novel approach towards labor market research, making novel data interoperable which has not been considered in previous literature. Our approach and pipeline is generic and could be easily extended to other languages. It also contributes to prestige research by widening the question of ascribed prestige to the question how information on occupations is collocated and what these contextualisations tell us about how occupations are seen.

This study investigated the patterns of undergraduate engineers’ information use as they transition from students to emerging professionals. We administered a questionnaire to 54 undergraduate engineers at a large research university, and compared the information use of 2nd and 4th year students. The results showed that 4th year students reported using more genres associated with professional contexts, such as technical reports, and fewer classroom-based genres, such as textbooks, than 2nd year students. However, a significant proportion of all students reported that they do not frequently use professional genres. These findings have implications for information literacy instruction by highlighting students’ need for further training on the relationship between genres and tasks.

Knowledge distance, representing the dissimilarity between different knowledge units, has been considered as an important dimension of recombination novelty and technological innovation. Previous measurements merely rely on the citation relationship and ignore their directions and weights. To fill this gap, this study proposes a network embedding method which not only capture the unequal citation relationship but also comprise multiple information to depict the distance. The results have shown that our method can accurately portray the knowledge distance in both scientific areas and technical fields.

Open Government Data (OGD) is essential for improving governance transparency, encouraging social participation, and inspiring collaboration between the public and private sectors. In order to realize the OGD Movement development in Taiwan via a practical perspective, this study adopts a qualitative approach to investigate 35 data practitioners’ behaviors based on the philosophy of grounded theory. The result indicates that the OGD practitioners’ behavioral pattern has seven phases, namely “Requirement Analysis,” “Functionality Design,” “OGD Awareness,” “OGD Access,” “Data Cleansing,” “Implementation,” and “Optimization and Maintenance.” Meanwhile, this study identifies “OGD quality” and “Interaction with the government” as significant necessities for the data practitioners’ development process. Finally, particular suggestions to improve the OGD utilization environment and further research recommendations are proposed.

This study explored whether harmonic counting, which emphasizes the position and role of authors in the author byline, produces researcher rankings (based on the number of articles published) that differ from those produced through full counting in multiple authorship–oriented research fields. An analysis of articles published during a 10-year period (2012–2021) by 377 chemical engineering professors or associate professors revealed a significant and positive correlation between full counting rankings and harmonic counting rankings. This finding indicates that when multiple authorship is the predominant trend for researchers within a given field, full counting can be performed to quickly determine researcher productivity rankings because it is more straightforward than harmonic counting. Future research should explore other research fields with diverse publishing trends.

The governance of health misinformation has been a hot topic in both social practice and academic research. Due to its proactive and timely nature, prebunking represents an emerging and efficacious intervention. However, previous research on prebunking primarily focuses on presenting arguments or techniques to the public in a direct and coercive manner, which remains limited in its scope and efficacy. This study aims to implement prebunking in social media by utilizing a milder approach, namely digital nudging. We conduct a web-based pre-experiment to test the effectiveness of warning, social and disclosure nudge, and obtain data from 104 participants. The preliminary results show that the warning and social nudge can mitigate the credibility of misinformation and decrease individual’s sharing likelihood. Furthermore, eHealth literacy acts as the moderator in the impact of social nudge. This study broadens the comprehensions of the misinformation governance and digital nudging, and furnishes practical implications for the implementation of prebunking in social media.

Scholars seek to translate research into practice through science communication, but the overwhelming use of English makes it challenging for some local community actors to access and implement research findings. Machine translation (MT) tools can help, but the underlying data-driven approach does not work equally well for all languages and research domains. If plain language summaries can be rendered more MT-friendly, this could make it easier for speakers of other languages to access research. This poster compares guidelines for plain language and guidelines for writing for MT to determine their compatibility and potential usefulness for creating reader- and MT-friendly plain language summaries.

This poster introduces an ongoing project to develop a scale for measuring information credibility on current and newly emerging interactive web platforms. The poster reports on preliminary findings from an initial phase in the project to generate an item pool based on an analysis of existing scales for credibility (n = 3) and empirical studies in the library and information science literature on web credibility assessments in the social media context (n = 19). Results show that in most papers analyzed (16 of 19; 84.2%), credibility was conceptualized as a one-dimensional construct and often measured with only one item (e.g., credible, believable), despite the common view among scholars that credibility is a high-level, multifaceted concept. The analysis also identified 59 semantically distinct items as an initial pool, which will be validated and tested with empirical data in subsequent project phases.

Data papers, a new class of scholarly publication emerging from the open-science movement, foster data discovery and reuse by offering comprehensive descriptions of research data. Yet, despite their promising growth, the role of data papers in scholarly communication remains underexplored. This study therefore investigates the perceived contributions and functions of data papers to scholarly communication by interviewing 14 data-paper authors op-erating in the field of natural science. Using conceptual frameworks adopted from Borgman (2007) and Van de Sompel et al. (2004), we identify four general functions of scholarly communication (i.e., legitimization; dissemi-nation; access, preservation, and curation; and rewarding). Additionally, our data lead us to propose that verifica-tion is a distinct scholarly communication, underscoring the importance of data papers in validating research find-ings in the context of ensuring research transparency. By elucidating the crucial role that data papers now play within the scholarly communication ecosystem, this study seeks to raise the academic community’s awareness of their fundamental position, as well as their co-existence with other forms of data publication, in advancing scien-tific research.

This preliminary study reports citizens' perceptions of using artificial intelligence (AI) in instrumental and value-added decision-making. We analyzed 5,153 responses from the Pew Research Center's American Trends Panel Survey, collected in 2021. The findings suggest that concerns outweigh excitement when it comes to adopting AI systems for complex value-added decisions while they welcome AI systems for instrumental tasks on behalf of humans.

This poster discusses Automated Research Workflows (ARWs) in the context of a FAIR data ecosystem for the science of science research. We offer a conceptual discussion from the point of view of information science and technology using several cases of “data problems” in the science of science research to illustrate the characteristics and expectations for designers and developers of a FAIR data ecosystem. Drawing from a 10-year data science project developing GenBank metadata workflows, we incorporate the ideas of ARWs into the FAIR data ecosystem discussion to set a broader context and increase generalizability. Researchers can use these as a guide for their data science projects to automate research workflows in the science of science domain and beyond..

Deepfake research has gained traction in recent years. There have been surveys that summarize work on the detection and generation of deepfakes. However, a more comprehensive and quantitative overview that encompasses both technical and non-technical areas is lacking. In this paper, we address this gap by utilizing BERTopic to discover deepfake research topics found in academic publications. Our results show that while detection techniques topics dominate the research field, other areas, such as privacy and legal research, offer potential avenues for further exploration. This study provides a clearer picture of current and potential research directions for deepfakes.

The development of research infrastructures (RIs) for social sciences and humanities (SSH) research is still incipient. Few of the existing facilities face several challenges that complicate researchers’ interactions with RIs and its digital tools and materials. This study explores the creation of an information service for conducting data-intensive SSH research by placing the end-user at the center. Particularly, the study investigated what factors affect the development of the RI and if this process is integrating all the information needs of end-users, putting a special focus on how these facilities should be evaluated. A qualitative analysis of 13 semi-structured interviews with SSH scholars and computer/data scientists revealed three themes: developing a RI, needs and expectations of the RI, relationship with user feedback and user interactions. Based on our findings, we were able to raise several design implications to create more successful and sustainable information services for supporting SSH research processes.

Generative artificial intelligence (GAI), a branch of AI capable of generating new content from human input, has the potential to impact every aspect of our society. However, as with any emerging technology, the benefits and detriments of GAI may not be evenly distributed, leading to digital inequality. Aspects like AI access, effective usage, and usage outcomes could define this emerging and evolving GAI divide. To fill the current research gap on GAI inequality, this study aims to empirically explore the factors that may contribute to a GAI divide to ensure that the benefits of this technology are distributed equitably throughout society. To address these challenges, an online survey will be conducted to examine both macro and micro-level factors that may potentially proliferate and ameliorate the GAI divide.

This poster highlights the connections between independent media, information activism, and disability through a case study of the podcast Death Panel and its listeners. Death Panel is a leftist podcast that explores issues of affordability in medical care and the dearth of public health resources. The ongoing project explores the independent media created by disabled people that concern their frustrations with societal and governmental neglect, and their desires to build community and a collective political movement for social change. Since the COVID-19 pandemic, the podcast’s listenership has grown substantially. Similarly, the number of members in the Death Panel Discord group has increased to over 2,000 members over the past two years. As of February 21st, 2023, there are 2,954 members in the Discord server. For many members, especially those who are disabled and chronically ill, the podcast’s Discord has served as a significant site of politicization and community-building during the COVID-19 pandemic. Through semi-structured interviews, this project will explore the political and activist implications of information for marginalized people.

Understanding the interactions between science and technology (S&T) is crucial for driving major innovations. Previous studies have typically focused on identifying scientific and technical topics separately and analyzing their association through semantic or citation. In this study, we propose a novel approach to identifying linked topics that directly reflect the interactions within the S&T domain. Our approach integrates semantic characteristics and citation relationships, allowing for a comprehensive analysis of the specific content and structure of these interactions. We test our approach using a dataset of 2,821 patents and 4,626 papers from the field of genetic engineering vaccines, spanning the years 1980 to 2020. The results demonstrate that our approach provides a more direct and detailed understanding of the content and structural characteristics of S&T interactions. This research contributes to the methodology of linked topics identification in the field of S&T, offering new insights and analytical perspectives for related studies.

In the past, high-tech enterprises aimed to apply for patents to protect their innovation and obtain a competitive advantage. However, Open Innovation models encourage enterprises to open and exchange knowledge to accelerate technology development because of higher innovation complexity. Nonetheless, the impacts of open knowledge on follow-on innovation are unclear. Tesla’s patent openness event provides a better opportunity to observe a causal relationship between open knowledge and follow-on innovation as it is an exogenous shock for the whole Electric Vehicle (EV) field. Against this backdrop, this poster uses patent data of Tesla and EVs before and after the open-patent events from Derwent Innovations Index (DII) between 2003 and 2020. The difference-in-difference regression results show that patent openness has an inhibition effect on follow-on patents and patentees and the high-value open patents will strengthen this inhibition effect. Surprisingly, once technologies were shocked by openness, more open patents would stimulate them to boost in the future.

This study investigates the impact of digital reading patterns on comics, specifically examining page-by-page and scrolling formats. We compare the unique layout characteristics of each format and scrutinize the level of reader engagement elicited by these two distinct types of comics. In this study, we conducted a between-subject, eye-tracking experiment and the following semi-structured interviews with 20 reader participants. Results highlighted that readers of page-by-page comics scan quickly, while scrolling comics require navigating through each panel. The research concludes that page-by-page comics enhance reading efficiency but increase cognitive burden, while scrolling comics provide more breathing room but may seem redundant. These findings contribute to understanding reader perspectives on comics reading behaviors, providing insights for the digital transformation of the comic industry.

The growth rate of older adult produces demographic shift in Bangladesh. The objectives of this study is to explore how public libraries in Bangladesh can work or support for positive ageing. This study is a small part of larger survey study of the public libraries in Bangladesh. Data were collected from two open-ended questions which were used in the larger quantitative survey. Out of 59 responses received from the public librarians, only 15 librarians responded to open-ended questions. The answers of these two open-ended questions were coded and analyzed. Public libraries do not offer exclusive services for the older adults but many of the general services are used by the older adults. However, public librarians have come up with significant ideas to support older people for promoting the positive ageing. Findings also focused how this helps to achieve some of the UNSDGs in the context of Bangladesh.

Online nonsuicidal self-injury communities commonly create and share information on harm reduction strategies and exchange social support on social media platforms, including the short-form video sharing platform TikTok. While TikTok's Community Guidelines permit users to share personal experiences with mental health topics, TikTok explicitly bans content depicting, promoting, normalizing, or glorifying activities that could lead to self-harm. As such, TikTok may moderate user-generated content, leading to exclusion and marginalization in this digital space. Through semi-structured interviews with eight TikTok users with a history of nonsuicidal self-injury, this pilot study explores how users experience TikTok’s algorithm to create and engage with content on nonsuicidal self-injury. Findings demonstrate that users understand how to circumnavigate TikTok’s algorithm through algospeak (i.e., codewords or turns of phrases) and signaling to maintain visibility on the platform. Further, findings emphasize that users actively engage in self-surveillance and self-censorship to create a safe online community. In turn, content moderation can ultimately hinder progress toward the destigmatization of nonsuicidal self-injury and restrict social support exchanged within online nonsuicidal self-injury communities.

This paper proposes “information borderlands” as a framework for bounding a unique information environment that is constituted by individual and community level practices, physical landscapes (natural and human-shaped), as well as large-scale sociotechnical systems and systems of documentality. Taking the borderlands of the U.S. Southwest as a unique case, existing research literature is reviewed and directions forward for future research are suggested.

Information triangulation is a complex practice involving seeking and assessment. Although a newer model, it has begun to be tested and expanded in subsequent research. This poster presents early results of a study of the information triangulation of 23 pregnant and postpartum people in Massachusetts, USA, who were making decisions about cannabis use. Interview transcripts and notes were analyzed using reflexive thematic analysis, in an emic-etic approach, and findings were compared against the six previously identified types of information triangulation. All six types were evident, although specific source types and pathways differed among populations. Further, participants described both explicit weighing of sources against each other and heuristics they used to determine source credibility.

Deepfakes are AI-generated multimedia that convincingly graft or replace one person's likeness onto another, resulting in remarkably realistic yet fabricated content. These sophisticated manipulations challenge users’ ability to discern factual visual information as they portray individuals engaging in actions and uttering words that never occurred. This research takes a unique approach by delving into the perspective of deepfake creators through the analysis of 15 tutorial videos on YouTube. By examining creators' information-sharing practices, this study aims to identify creators' motivations for disseminating knowledge on deepfake creation and their understanding or lack of understanding of the ethical implications of sharing such information. Achieving such an understanding can play a vital role in informing the development of regulatory measures. By examining creators’ intentions and comprehending their societal impact, patterns, and characteristics, this research can contribute to the creation of effective detection technologies and strengthen content platform guidelines. Analyzing deepfake creation tutorials can facilitate the implementation of targeted and comprehensive regulatory measures, enhancing protection against potential risks and misuse.

This poster presents research investigating the health information use of older adults with diabetes in the context of the COVID-19 pandemic. Data were collected using semi-structured interviews and were analyzed using thematic analysis. Results-to-date showed self-reported changes in using health information during the pandemic; that is, some participants endeavored to protect themselves during the pandemic, they carefully made health-related decisions, and they had a challenging time following their caregivers’ suggestions (e.g., taking medicine and exercising) even though they knew they should. However, other participants reported no changes during the pandemic.

We are living in a world where a lot of people maintain constant connectivity through sending and receiving messages and calls. Contemporary research has focused on how smartphones disconnect us from our physical environment. Yet, in a digital disconnect, almost daily, people choose not to respond to certain messages or calls, which can make the sender anxious and adversely affect their communication. The first phase of this project consisted of an interview study of 24 participants which led to publications covering the sender and receiver perspectives. A follow-up large-scale survey of 4,001 respondents was carried out to investigate the receiver’s reasons for not responding including the role of power, the psychological impact on the sender, and other communication. Theories on information avoidance, power, interpersonal deception, and emotions inform this study. Preliminary findings are shared here. The findings will inform the fields of information science, communication, psychology, and mental health, and should help software designers build tools to help users communicate their context to each other.

This research in progress presents a deep learning-based approach to identifying named entities, including geographical locations and poetic imagery in ancient Chinese poetry. By leveraging association rule mining, this study establishes a connection between historical events, spatial-temporal trajectories of poets, and sociocultural phenomena of the age. From the perspective of digital humanities, we hope to be able to provide new evidence for socioeconomic status, cultural openness, or historical events at different ages, and to picture a better view of the development of classical Chinese.

Artificial intelligence (AI) technologies shape the ways information is created, delivered, accessed, evaluated, and used, but empirical research on AI and human information practices has remained scarce. This study makes use of a nexus analytic research strategy and co-research methods to study the impact of AI systems in the everyday lives of young people with an emphasis on the information practices that are reshaped due to the use of these technologies. The overall aim is to better understand how AI systems impact young people’s information practices and in conjunction to them, their autonomy, wellbeing, possibilities to participate in society, and access to rights. The empirical research will engage young Finnish people in meaningful conversations about their everyday encounters with AI systems and, using co-research methods, explore how these interactions shape their information environments, practices, agency, and identities.

Understanding the aspects of a restaurant that contribute to people's sentiment is crucial for restaurant owners and marketers to improve customer experiences. A sentiment ontology for restaurant review sentiment analysis was proposed in this study. The proposed ontology identified six aspects of a restaurant evaluation: food, service, ambience, cleanliness, location, and price, based on 1,000 annotated Yelp data. Applying the proposed ontology in the annotated Yelp data showed that it helped improve the sentiment analysis accuracy by 11.2% on average. We believe that this study provides insights into the key factors that contribute to restaurant review sentiments.

Scientific breakthroughs have the potential to revolutionize the course of research and shape the trajectory of scientific knowledge. This study investigates the characteristics of Disruptive Citing Papers (DCP) and Consolidating Citing Papers (CCP) associated with Nobel-winning scientific breakthroughs, aiming to provide insights into the mechanisms of knowledge creation and dissemination. By analyzing a dataset of Nobel-winning papers and their citation networks, we find that Nobel-winning papers tend to attract a higher proportion of DCP compared to CCP. However, CCP exhibit a higher impact, as evidenced by their citation counts and likelihood of becoming hit papers. Furthermore, DCP are associated with larger research teams, highlighting the collaborative nature of disruptive research, while CCP employ a higher degree of professional language style characterized by shorter titles and specialized jargon. These findings deepen our understanding of the role played by disruptive and consolidating impact in scientific breakthroughs, shedding light on the dynamics of knowledge creation and dissemination in the scientific community. This research contributes to the broader understanding of scientific progress and provides valuable insights for researchers, policymakers, and stakeholders in the scientific ecosystem.

In this poster, we report the results of a research study examining the presence of demographic data and other community-based data and their grouping in visualization on the COVID-19 dashboards developed by the 50 state governments across the USA and the government of District of Columbia. It was found that while all dashboards included some level of demographic data, there is notable inconsistency in the groupings, and a very limited number of the state-level dashboards included visualization filtering beyond the basic demographic attributes. Several dashboards included additional data grouping capabilities such as underlying health conditions, residence/business clusters, employment status, or social vulnerability index. Both the inconsistency/gaps in demographic grouping and the fact that only handful dashboards contained further community-based information shows the lack of awareness of state government on the importance of incorporating detailed grouping in demographic data as well as other community-based datasets. Public health dashboards, including those reflecting emergency or crisis situations such as COVID-19 dashboards, are in serious need to accurately, comprehensively, and inclusively represent and display the data patterns of all members of the community, especially the often overlooked and marginalized communities.

This study addresses the need for a shared understanding of humanities data to enhance data curation. Through interviews with 27 scholars, it identifies two ways scholars conceptualize data - by format or role in research. It highlights three unique aspects of humanities data: diversity in required processing levels, significance of authorship and perspective, and the dual role of tertiary sources. The study suggests prioritizing provenance, facilitating data documentation, curating tertiary sources for wider use, and establishing scholarly communication mechanisms for effective data curation.

During global health crises, identifying the key factors of the misinformation dissemination process on social media can provide decision support for public health management. Drawing on the elaboration likelihood model (ELM), this study characterizes the effects of content types and social capital of social media users on the virality of misinformation related to the COVID-19 pandemic. We used scale, depth, and width to quantify the extent and structure of the virality of misinformation spreading on social media. The findings reveal that both the social capital of users and the content types have major influences on the dissemination of misinformation. Surprisingly, we discovered that the number of followers a user possesses has a varied influence on the dissemination scale, width, and depth, demonstrating the importance of considering dissemination structure.

The present study discusses the methods and outcomes of a project conducted at the University of Brasília with the aim of providing information services to the community of the Federal District (Brazil) on COVID-19 prevention and vaccination, as well as addressing domestic violence. Employing social marketing strategies, such as content marketing and viral marketing, the project sought to disseminate pertinent information and engage the target audience through social media platforms, primarily Instagram. The content marketing approach entailed the creation and distribution of reliable information utilizing authoritative sources and visual resources, such as illustrations, graphs, infographics, and diagrams. Simultaneously, the viral marketing strategy endeavoured to identify emerging subtopics of interest by leveraging the Google Trends tool and relevant commemorative dates. These integrated strategies yielded a remarkable 700% surge in the project's Instagram account reach alongside heightened audience engagement. The study concludes that community information services should prioritize the dissemination of contextually relevant information for various population segments, particularly those that are historically marginalized.

A core aspect of the scientific process is the verification of the credibility of findings. In research with a qualitative approach, there is an ongoing discussion about the most effective method to validate results. Discussed in this poster is the efficacy of community validation as a novel method to determine trustworthiness of research findings, with reference to a study into the adoption and use of public library services by forced migrants that was completed in 2022. Community validation involved research with ‘not-yet-participants’ to explore the accuracy of researcher findings from previous interviews. The use of community validation here resulted in increased the interpretive power of initial results, and of new results to develop understanding of the topic. It is concluded that community validation expands upon current methods of determining trustworthiness in research with a qualitative approach. It is particularly useful for research with participants who are not the population of direct interest, but informants who supply data based on their own observations of the members of the target population, such as LIS practitioners.

Data quality assurance (DQA) is critical to research data sharing and reuse. There has been a growing recognition of data transparency, reproducibility, credibility, and validity in research. Although the research data curation literature is large, it lacks data quality theory-guided examinations of DQA practices in research data repositories. This poster paper reports on the preliminary findings of a larger study that examines DQA practices in research data repositories, including their use of DQA ontologies, standards, and metadata vocabularies. In particular, the paper examines two quality standards and an ontology for their conceptualization of DQA activities and their structure. The authors used the findings of the analysis and the data quality literature to synthesize an initial model of a DQA process in research data repositories that conceptualizes three DQA activities: evaluation, intervention, and communication. This paper can inform the development of ontologies and best practice guides for designing and evaluating DQA workflows in research data repositories.

We propose a bottom-up, data-driven pipeline to uncover the structure of biodiversity subject metadata using a combination of text mining approaches. In this study, we analyze 721,035 subject terms in the Biodiversity Heritage Library (BHL). We utilize named entity recognition and word-embedding methods to systematically label and group terms based on their vector-space distances. The results show that the subject terms from BHL are clustered into several prominent themes relating to environmental regulations, geographic locations, organisms, and subject access points. We hope that our approach can serve as a first step to group similar subject terms together in large-scale, constant growing digital collections with aggregated metadata from multiple sources. Ultimately, we hope the next phases of this project can become a basis for biodiversity digital libraries to standardize their vocabularies.

Large amounts of samples have been collected and stored by different institutions and collections across the world. However, even the most carefully curated collections can appear incomplete when aggregated. To solve this problem and support the increasing multidisciplinary science conducted on these samples, we propose a method to support the FAIRness of the aggregation by augmenting the metadata of source records. Using a pipeline that is a combination of rule-based and machine learning-based procedures, we predict the missing values of the metadata fields of 4,388,514 samples. We use these inferred fields in our user interface to improve the reusability.

All over the world, libraries are facing fraught political and economically insecure times; books are being banned, marginalised communities are being protested, and Library and Information Professionals (LIPs) are struggling to narrow the digital divide to support a well-informed society. This poster explores the connection between the competencies required by two professional library organisations and the reality of Irish library and information professionals in their actual work context. Our initial data collection consisted of a quantitative survey with 897 Irish library-based participants. The preliminary analysis suggests the need for the strengthening of an Irish national code of professional and ethical framework, which would be of benefit to LIPs and Irish society. These ethics and values statements are the foundational pillars on which knowledge, skills, and professional abilities are built.

The prevalence of deepfake technology has led to concerns about the ability of people to spot real videos from fakes. We address these concerns through a diary study to elicit strategies people use to identify deepfake and real videos. Results indicate that different strategies were associated with deepfake and real video identification. Interestingly, popular strategies were often not as effective as less popular ones. Implications of our work are discussed.

This poster considers the role that information and context may play in health management. We employ a well-known taxonomy of techniques for promoting behavioral change to consider how social media authors describe their recovery from substance use. We harvest discussion posts from subreddits, or discussion forums, about alcohol, cannabis, and opioids, and perform content analysis to identify behavioral change techniques (BCTs) described in the content. We then consider the role that the context of information use plays in these BCTs, as well as how interventions and technologies might be leveraged to better support the recovery process.

Scientific retractions can be an indicator that misinformation is present in a research paper. Retractions are therefore an interesting research object to explore news coverage of misleading scientific information. This poster presents how problematic research (before and after retraction) is portrayed in news outlets, the impact of the retraction on these reports and the relationship of retracted scientific results and the spread of false information. Starting from a list of 270 retracted COVID-19 papers from the Retraction Watch blog , we analysed news articles of 16 retracted publications, that were immensely discussed in journalistic formats. By presenting three different use cases, we show how misinformation emanates from retractions and how problematic research is presented in media. Our research contributes to a better understanding of how retractions are used and perceived in propagating scientific misinformation on one hand and in mitigating it on the other.

The aim of this study is to investigate the reciprocal relationship between negative emotions and protective behaviors of older adults and the moderating effect of online health information seeking (OHIS) change on the relationship. Based on a sample of over 20,000 individuals from the Survey of Health, Ageing and Retirement in Europe project, the study used a cross-lagged panel design(CLPD) on 2 waves of Corona Survey to analyze the relationship between negative emotions and protective behaviors with OHIS change as a moderator. The findings suggest that the number of negative emotions can positively predict protective behaviors, and OHIS can have moderating effects on negative emotions and protective behaviors. The result pinpoints the substantial potential of mental health and OHIS to combat infectious diseases in this vulnerable population.

Access to higher education is a form of capital that is not always equally distributed. First-generation doctoral students (FGDSs) face unique challenges and barriers which can make it difficult for them to navigate academic and institutional structures and access key resources and support. We present preliminary findings from interviews with 8 FGDS participants who pursue doctoral degrees in the field of information science in Canada. Interviews focused on identifying FGDSs’ information practices in their pursuit of higher education. An inductive thematic analysis revealed the diverse information needs and sources utilized by this understudied population. The results provide insights about affective dimensions of information seeking and the role of mentors as key information intermediaries in supporting more diverse, inclusive, and equitable space for FGDSs. The poster concludes with implications for practice to improve the interfaces between FGDSs and higher education institutions as well as the broader academic landscape.

Author keywords attached to academic papers are often used in intellectual structure analysis. However, the length and selection criteria for keywords vary across publications and, even some publishers do not require keywords for their articles. To explore the opportunity to overcome such keyword inconsistency issues, this study compared author keywords from papers focused on the digital divide with those extracted using the language models, BERT and ChatGPT. Preliminary findings reveal structural variations across the keyword networks and suggest a potential need to revisit keyword-based research. Future research will expand the scope of the dataset and conduct an in-depth analysis of keyword patterns across the language models.

Journal policies play a crucial role in facilitating the sharing of research data. This study examined the Data Sharing Policies (DSP) and Supplemental Materials Policies (SMP) of ten journals from 22 disciplines in 2023, comparing the findings to those of previous surveys conducted in 2014 and 2019. Among the 220 journals analyzed, 91.4% implemented DSP, whereas 95.0% adopted SMP. Compared with the surveys conducted in 2014 and 2019, a greater number of journals exhibited more stringent requirements for DSP, particularly in the fields of Engineering, Economics & Business, and Computer Science. Although the percentage of journals that accepted supplemental materials increased, only 12.7% strongly required their submission. Furthermore, this study compared the changes in Journal Impact Factors (JIF) between journals that adopted DSP (either requiring or recommending it) and those that did not have DSP in 2019. These findings suggest that DSP has a significant effect on JIF in the Economics & Business and Psychiatry/Psychology fields.

YouTube has become an important platform for political communication and information, but little research has been done on users' behaviors regarding political content. This study uses digital trace data and survey data from over 9,000 users to investigate political information consumption, content, and network on YouTube. This study also examines political polarization and partisan bias in videos. By exploring the intersection of digital culture and political attitudes, the study will contribute to a better understanding of the role of digital media in shaping people's understanding of politics.

Investigating the writing styles of different disciplines is crucial for comprehending the scientific language in scholarly communication. This study examines the variations in scientific writing styles across disciplines using a large-scale analysis on 14 million abstracts from the Microsoft Academic Graph database. Writing styles are quantified in four aspects: symbolic, lexical, syntactic, and readability. The t-tests were employed to explore potential trends in the evolution of writing style and investigate linguistic differences across disciplines. Results indicate an overall trend towards increasing complexity and informativeness in writing, with significant changes observed in Biology, Chemistry, Computer Science, and Psychology. Disparities of linguistic features in writing styles were observed across disciplines. The findings shed light on the distinct language use in scholarly communication across disciplines.

Commentaries are derivative texts formed by commentators’ interpretations of classics texts, which not only reflect the commentators’ understanding and values in their era but also play an irreplaceable role in contemporary people's understanding of classics texts. At present, the literature in which commentaries are written collected by the library is organized in commentator-centered and it is difficult to efficiently collect all the commentaries of the same classic texts and analyze the citations in the annotation texts. The development of Semantic Web technology has changed the way of knowledge representation and provided new ideas and methods for the organization and sharing of commentaries. We use the seven-step method to design the HERU ontology. Finally, we conduct a semantic annotation experiment using some commentaries of the Analects of Confucius as an example to illustrate the practicality of the ontology in constructing annotation diachronic evolution and citation data. This study is of great significance to the inheritance and interpretation of Chinese classics and has reference significance for the semantic management between classic texts and their commentaries in other countries.

Stack Overflow (SO) represents one of the most vibrant Question Answering Communities (QACs), providing a crucial platform for developers to pose and respond to questions. A unique attribute of SO is the existence and retention of duplicate questions, these questions are preserved due to their potential for furnishing additional insights or suggestions. In this paper, we delve into the study of duplicates within SO, with the objective of unraveling their positive value, particularly through the lens of knowledge networks and the evolution. We propose a categorization of knowledge evolution within QACs into two key dimensions: depth and breadth. Our exploration reveals that duplicate questions play a constructive role in fostering both the depth and breadth of knowledge evolution. This finding illuminates the underestimated value of duplicate questions, underlining their significance for the ongoing expansion and enrichment of knowledge within QACs.

We observe a growing universe of machine-readable knowledge organisation systems (KOS) or even wider ‘semantic artifacts. We see at the same time, various attempts to bring semantic artifacts together via registries, catalogues and via cross-walks among ontologies. This poster reflects how newest research on semantic interoperability informs current practice for research data repositories and registry service providers. We focus on the domain of humanities and cultural heritage, using different examples from Europe and the Netherlands: The European Archaeological Data Infrastructure Ariadne, services of the Dutch Cultural Heritage Network and two Data Stations (for Archaeology and for SSH) hosted at DANS-KNAW. For those cases we report on current efforts to include ‘semantic artifacts’, together with opportunities and challenges. This poster is a practice report combined with an invitation to reflect about the context of semantic interoperability itself.

In the "intangible cultural heritage" digital humanities project, crowdsourcing plays a key role. Based on the related theories of gamification and narrative, it takes the traditional Chinese opera Henan Yuediao as a case, designs a humanistic contextualized crowdsourcing platform based on interactive digital narration, and tests its usability and feasibility. The research is conducive to promoting the in-depth mining and utilization of archives.

Based on the privacy calculus theory, users’ decision-making on privacy disclosure was traditionally viewed as a trade-off between perceived values and privacy concerns. However, recent studies suggest that the impacts of users’ cognitive style and platform trust cannot be ignored in the context of social media platforms. From a configurational perspective, this study explores how these factors collectively affect users’ privacy disclosure. Data from 452 respondents on a Chinese social media platform were collected through an online survey. The results suggested that users’ decision-making on privacy disclosure in social media is a complex process involving different configurations. For field-dependent individuals, the trade-off between perceived values and privacy concerns is less important than the role of cognitive style. For field-independent individuals, beyond the trade-off between perceived values in different dimensions and privacy concerns, their decisions are also contingent on platform trust.

Although there is much discussion of the theory/practice gap in library and information science, there is little research about how librarians use formal theory in their practice. Using a card sort and interviews, we explore the extent to which public librarians encounter or use theoretical concepts and models from information behavior in their reference practice. Results from our pilot study show that our procedures, including plain language descriptions of theoretical concepts and models printed on cards, are effective for eliciting examples of theory in practice. Librarians are clear about which concepts are relevant to their practice and can easily provide examples of real-world application. Results of the study will suggest which theoretical concepts might be most important for instructors to teach to students in reference courses and which translate best from the research to the practice environment. In addition, the examples provided by librarians of how they use the concepts in their practice will be useful for instructors as they try to engage students in learning.

Recent research on personalized retrieval technology has been actively conducted to meet the needs of users for seeking adequate information. To refine the retrieval, researchers are considering user behavior patterns in a variety of ways. In this study, we use eye-tracking metadata to predict users’ levels of comprehension as textual evidence for IR processes. Furthermore, we incorporated eye-tracking metadata on the Automated Readability Index (ARI), a readability assessment tool of an English text. Our research is largely divided into two tasks: i) comprehension evaluation task (CET) and ii) comprehension-based retrieval task (CRT). In the CET task, for predicting the comprehension level, we applied various regression models. Among them, the Voting regressor demonstrated the highest performance with a Spearman’s 𝜌 of 0.68. In the CRT task, we incorporated the level of comprehension predicted in the CET task and ARI score into the ranking results. We derived a sentenceBERT to find the relevant text for a query and the Normalized Discounted Cumulative Gain (nDCG) for evaluating the CRT task. The nDCG score for Comprehension Level only and that with ARI together were 0.65 and 0.78, respectively. Thus, applying ARI resulted in a higher nDCG score compared to comprehension level only.

Information behavior research to date has mainly focused specific cases or representative surveys at the individual level, because each individual has unique contexts that shape their behavior. However, they have not fully benefited from aggregate-level analyses due to mainstream theories’ focus on a contextualized understanding of information. To address this gap, we adopt the theory of local information landscapes, that focuses on the material aspects of community dynamics, and analyze national-level aggregate data on book circulations in public libraries across South Korea. By examining the relationship between socioeconomic status and public library book circulation, we discuss the potential to develop scalable theories and relevant data-driven approaches in information behavior research.

This study aims to explore the factors related to young women's Internet use for reproductive health information, specifically focusing on female college students. The research model is based on the Health Belief Model (HBM) and the Theory of Planned Behavior (TPB). A convenience sample of female college students who have recently used the Internet for reproductive health information will participate in an online survey. The survey will collect demographic information, background details on Internet use for reproductive health, and perceptions of reproductive health behaviors. Data analysis will examine correlations among the perceptions and their association with the intention to continue using the Internet for reproductive health information. The findings will contribute to understanding female college students' influential factors and information behaviors, informing the development of educational programs, intervention strategies, and online resources to improve reproductive health outcomes and empower young women.

Introduced by Microsoft in February 2023, Bing Chat is a feature of the Bing search engine that integrates an OpenAI large language model (LLM) customised for search (Mehdi, 2023a). This poster compares the outputs of Bing Chat and a standard existing search engine (DuckDuckGo) in response to identical keyword queries and corresponding natural language (NL) questions. Specifically, we examined: (1) the length of Bing Chat’s responses and DuckDuckGo’s first page of search results, by number of website links; and, (2) the length of Bing Chat’s textual summaries, by number of website links. We found that, on average, significantly fewer websites were linked to in Bing Chat’s responses compared to DuckDuckGo’s search results. Our findings have important implications for website operators, who may receive less traffic and ad revenue if LLM-enabled search engines are widely adopted in the future. Human-Computer Interaction (HCI) will inevitably face the need for more research on human information behaviours adaptations in response to the changing search paradigm.

The current study developed a data literacy instrument with 20 contextual questions and conducted a multi-mode survey with 232 pre-service secondary teachers in a university system. Three-way ANOVA was used to test whether pre-service teachers with different characteristics (i.e., field of study, library use, and Internet use) performed differently on data literacy. Pre-service teachers got a decent overall percentage score. They performed better in “identifying problems” and “analyzing data” than in “transforming data” and “collecting data.” While pre-service teachers in humanities and social sciences performed better in “identifying problems,” those in sciences performed better in “analyzing data.” Humanities pre-service teachers with high Internet use scored significantly lower; social science pre-service teachers with high Internet use and low library use scored significantly lower; science pre-service teachers with low library use scored higher. Implications for future data literacy education and instrument design are discussed based on the preliminary findings.

The ICT advancement and people’s increasing cultural needs have stimulated China’s online form of cultural activity organization. With highlights on such a new trend, this paper analyzes the impact of the newly-invented online activity organization form on the participation ratio of parent-child cultural activities, which are vital in China’s public cultural service system. Regression analysis indicates that the online activity organization form reduces the impact of limited available tickets and web browsing. It also relaxes the special time limit for public participation. Such an online form of activity organization deserves further attention considering its flexibility and convenience valued by the public.

Innovation Open Data Contest (IODC) is an effective way to take advantage of public efforts to realize the great potential value of open data in the field of digital humanities. Previous literature focusses more on the challenge open data contest rather than innovation open data contest. Given this, understanding the underlying factors motivating participants to actively engage in the contest is necessary. Based on the task affordance theory and self-determination theory (SDT), this study aims to identify and examine how task presentation affordances of IODC influence participants' motivations and thereby shape their level of effort. We employ partial least squares structural equation modeling (PLS-SEM) techniques to analyze the responses from 215 individuals who have previously participated in the IODC. The findings indicate that participants' level of effort in the IODC is contingent upon their perception of relatedness and competence. Moreover, hedonic affordance and connective affordance positively influence participants' perceptions of relatedness and competence. Our findings contribute to the extant literature by proposing a theoretical model to understand the participants’ motivation and have practical implications for IODC’s organizers.

Understanding and defining cybersecurity-related concepts for the general public can often prove challenging due to their inherent complexity; consequently, visual imaginary emerges as an effective device for articulating and elucidating these abstract ideas. In this study, we leveraged an adapted version of the existing iSquare approach to explore people’s understanding of cybersecurity. We collected 499 iSquares from a broad-spectrum backgrounds of participants attending a prominent international cybersecurity exhibition in Taiwan. Our preliminary findings revealed that metaphors of physical security and warfare were the most frequently employed in participants' conceptualizations of cybersecurity. Participants’ work roles were also observed to influence their respective attitudes and perspectives as expressed in their pictorial representations of cybersecurity.

While accessibility is a core value of the American Library Association (ALA) and represents an important factor in striving for diversity, equity, inclusion, accessibility, and social justice (DEIASJ), coverage of accessibility varies widely in programs intended to equip future LIS professionals to serve patrons with disabilities. This poster presents preliminary findings from a larger research project examining library and information science curricula at 77 North American colleges and universities. Through thematic analysis of course descriptions and syllabi, the researchers found that digital topics in accessibility are addressed more frequently than physical ones. Further, LIS curricula largely overlook the needs of disabled patrons when covering accessibility. These factors present challenges for future LIS professionals serving patrons with disabilities.

In Spring 2020, the murder of George Floyd catalyzed calls for racial justice across the United States, ushering in a series of organizational and institutional responses. One response from Yelp, a popular review-based platform, included its addition of a searchable Black-owned attribute, a tag intended to signal its solidarity with Black businesses and communities. Analyzing a repository of over 300,000 Yelp reviews from Black and non-Black-owned restaurants in Los Angeles, CA, USA and Detroit, MI, USA, we pose the following question: How did the addition of the Black-owned tag impact the online reputation of Los Angeles and Detroit Black-owned restaurants on Yelp? To examine this, we use restaurant review counts and average star ratings as proxy measures for online reputation and track these metrics over the year following the platform design intervention. We find that the addition of the Black-owned tag did not create positive, sustainable change for Los Angeles and Detroit Black-owned restaurants. In all, we call attention to the disparate impacts of platform design features on users and communities, especially for Yelp and digital platforms seeking to effect racial justice through novel platform design interventions.

This study delves into the realm of information behavior research, examining the attitudes and information-seeking patterns of Polish and Ukrainian parents regarding vaccination. Amidst rising vaccine skepticism globally, understanding how individuals acquire and evaluate information about vaccinations is crucial for effective public health communication. Through in-depth interviews and surveys, the study examines how parents acquire information about vaccinations, evaluate the credibility of this information, and identify cognitive authorities that enhance information credibility. Preliminary findings demonstrate distinct information-seeking strategies between pro-vaccination and skeptical parents. Parents in favor of vaccination tend to rely on a single source, typically their doctor, emphasizing the authority associated with their role. In contrast, skeptical parents consult various sources, prioritizing close personal relationships and shared experiences. In contrast, skeptical parents consult various sources, prioritizing close personal relationships and shared experiences. Moreover, opponents of vaccination exhibit a stronger affective dimension when assessing source credibility. The study also highlights the distinctive use of books and articles among vaccine opponents, despite their general mistrust of science. This study contributes to the field of information behavior research, offering insights into parental information-seeking dynamics, and their implications for public health policy and communication strategies.

This research seeks to identify critical factors that have an impact on government knowledge base adoption, which is based on the local government of Yi Chang, a typical third-tier city in China. Firstly, the knowledge base and its development in Chinese local governments are introduced. Secondly, to build the adoption model, factors from the UTAUT and TOE framework were integrated and trust to knowledge base and intention to knowledge reuse are introduced. Thirdly, a questionnaire is designed based on the model and distributed to civil servants from Yi Chang local government. Finally, the proposed model is validated, and the collected data is analyzed by PLS-SEM. The results show the factors (effort expectancy, social influence, competitive pressure, trust to knowledge base, intention to knowledge reuse) have a positive impact on the adoption of knowledge base in Chinese local governments.

This study introduces and examines the “Multi-dimensional Interaction-Attitude-Usage Model” (MIAU Model), drawing from cross-disciplinary theories. The MIAU Model proposes dimensions of interactive data retrieval, including resource, technology, context, and dual-process-based cognition, and explores their relationships with user attitude toward systems and usage intention. To test the MIAU Model, a structural equation modeling analysis was conducted on the questionnaire data. The results support the proposed model. The MIAU Model suggests that resources, technology, contexts, and individual characteristics directly impact data searchers’ dual-process-based cognition. System 1-based cognition has a direct influence on system 2-based cognition. Moreover, dual-process-based cognition and contexts directly affect attitude and usage intention. Additionally, resources, technology, contexts, and individual characteristics indirectly affect system 2-based cognition through system 1-based cognition, and similarly, these factors indirectly influence attitude and usage intention through dual-process-based cognition.

People with shared health conditions, including contested conditions like central sensitivity syndromes (CSSs), are utilizing TikTok to both seek and tell visual illness stories—and to co-create health information in creative, mimetic, and platform-specific ways. This poster presents results from a pilot qualitative content analysis study of 100 TikTok videos and comments by people personally experiencing #fibromyalgia, a pilot codesign session with a CSS TikTok community member, and late breaking results from a dissertation in progress. These studies are the first stages in the goal to understand and support these embodied, creative, and often-collective storytelling abilities, necessary within the CSS community experiencing invisibility, stigma, and difficult diagnoses. The visual poster format enables discussion, with examples of TikTok videos illustrating 7 identified themes, iconographic elements, and examples of reoccurring creative choices and collective storytelling features. Last, of broader interest in our field, this poster presents methodological challenges and sparks for discussion of best methods for TikTok research, including: (1) a novel sampling approach addressing gaps in current research and the slippery definition of TikTok communities, (2) a brief description of preliminary codesign findings and the method’s promise in this domain, and (3) discussion of critical disability studies perspectives guiding this research.

Drug-Drug Interaction (DDI) may affect the activity and efficacy of drugs, potentially leading to diminished therapeutic effect or even serious side effects. Therefore, automatic recognition of drug entities and relations involved in DDI is of great significance for pharmaceutical and medical care. In this paper, we propose a generative DDI triplets extraction framework based on Large Language Models (LLMs). We comprehensively apply various training methods, such as In-context learning, Instruction-tuning, and Task-tuning, to investigate the biomedical information extraction capabilities of GPT-3, OPT, and LLaMA. We also introduce Low-Rank Adaptation (LoRA) technology to significantly reduce trainable parameters. The proposed method achieves satisfactory results in DDI triplet extraction, and demonstrates strong generalization ability on similar corpus.

There is increasing recognition of the challenges academics working on short-term contracts experience, as well as universities’ increasing reliance on their labor. While discussions of these issues have expanded, there is a lack of empirical research around the information experiences of contract academic staff. This poster reports on initial findings of a qualitative research project that interviewed 34 contract academic staff (CAS) from across Canada, exploring institutional provisions of information, information practices in the workplace, and how marginalization and social inclusion influence workplace information practices. We present preliminary findings in three themes: precarity and uncertainty necessitate holistic and situational understandings, exclusion and isolation are enacted and experienced on many levels, and uncertainty as a barrier to investment and furthering careers, exploring what these findings mean for contract academic staff members.

This study represents our preliminary work focused on the health information seeking of caregivers of ovarian cancer (OvCa) patients and survivors. Previous research has predominantly focused on OvCa patients' needs, while neglecting caregivers' needs independently. Through the analysis of social media posts by OvCa caregivers, this study explores seven categories of information needs desired by caregivers and examines how these needs evolve throughout the disease trajectory. By distinguishing between caregiver-specific needs and those on behalf of the patient, our study contributes to the development of a holistic caregiver support framework.

This study uses the UK as an example to explore the structure, content and governance network of scientific data ethics policies. Adopting grounded theory (GT) and Social Network Analysis (SNA), Nvivo12 is used to analyze and summarize the structure and content of scientific data ethics policy, and Ucinet and Netdraw are used to map governance network reflected in policy. Results indicate that the structure covers governance context, subject and measure. The content of governance context contains context description and data ethics issues analysis. Governance subject consists of defining subjects and facilitating their collaboration. Governance measure includes governance guidance and ethics governance initiatives in the data lifecycle. Governance network indicates that research institution plays a central role in ethics governance and the core of the governance content are ethics governance initiatives and governance guidance.

New media platforms have enhanced the efficiency and diversity of information dissemination, providing new possibilities for the dissemination and promotion of academic papers. Currently, a large number of Chinese academic journals from different disciplines have established WeChat official accounts to promote their papers. This study examines WeChat official accounts from three disciplines: social sciences, natural sciences, and medicine. We analyze the existing paper promotion methods employed by these academic journal official accounts from four dimensions: content presentation format, number of papers promoted in a single post, interactive forms, and publishing time. The findings reveal that the current promotion methods for academic papers on WeChat official accounts are relatively limited, with low utilization of multimedia content. Therefore, there is a need for further improvement in new media promotion for academic papers.

The trend of multilingualism in the web environment has put new demands on the digitization of cultural heritage. However, minority languages in cultural heritage have received little attention in China. This study selects traditional Tibetan festivals, a representative minority cultural heritage in China. Based on constructing a Chinese-Tibetan bilingual ontology, the cultural and tourism data of Tibet are linked. By further constructing a knowledge graph, a Chinese-Tibetan Bilingual Tibetan cultural service platform with traditional Tibetan festivals as the core is established.

Data sharing has been an extensively discussed subject in both the information science and biomedicine communities. However, the two communities do not seem to speak to each other. Such an absence of exchange of perspectives can lead to siloed knowledge, duplicated work, and hindered collaboration. To uncover existing knowledge gaps, this poster depicts the bibliometric relationships between relevant literature in the fields of information science and library science (ISLS) and biomedicine and health sciences (BMHS). The findings demonstrate stronger bibliographic couplings within ISLS publications and between ISLS and multidisciplinary science publications, compared to the BMHS group. In addition, the examined ISLS and BMHS publications exhibit distinct topical foci on open science. This poster is expected to serve as a first step toward stimulating more conversations in the information science community, as well as bringing the discussions to the biomedicine community in the near future.

In January 2023, heavy California flooding prompted users to capture and share video footage of their impacted surroundings. This preliminary study uses this crisis event to compare commenting behavior across three video content formats: YouTube videos, YouTube shorts, and TikTok videos. Using network and regression analysis to study 45 videos across these three formats, we find that users commented and replied to others more on YouTube than TikTok despite TikTok videos having more views than YouTube videos. Additionally, we find the most vibrant comment behavior under YouTube shorts. This work provokes additional research to understand the exact ways in which platform design and affordances can influence crisis communication around a specific event.

The application of AI(artificial intelligence) in libraries is not only the result of the development of technology, but also the choice of libraries to improve their service. However, how to better integrate libraries and AI still needs further exploration, and libraries also need guidance in implementing AI technology. This study uses a systematic literature review method to analyze the literature on the application of AI in libraries published before 2023. Based on sorting out the application of AI in libraries, this paper summarizes and analyzes the practice and attitudes of applying AI in libraries. We find that there is a broad prospect of AI applications in libraries, but the current application is scattered and lacks a comprehensive view. There are different attitudes towards the application of AI in libraries and it is important to learn about different views.

The poster discusses varying data collection approaches in investigation of an organization’s data governance practice of a single case study. The aim is to promote transparency in data governance practice on a case-by-case basis and to unveil the circumstances of adapting approaches in data collecting. Four distinct data collecting approaches were utilized in this study, each tailored to diverse needs and scenarios, including group interview, design-thinking workshop, semi-structured interview and field study. Clarifying the rationale of taking certain approaches, the research team aims for providing the benchmark for future data governance research.

This study investigates the practice of data-driven decision-making in higher education institutions in Ethiopia. It presents the results of a mixed-methods investigation that was conducted at two public universities in Ethiopia. Data was collected via a survey questionnaire from 91 faculty members, nine ICT experts, four higher education registrar experts, and 194 graduating undergraduate students. The study also involved interviews with two directors from the university's education program relevance and enhancement office and four senior officers from library departments. The findings revealed that the majority of the instructors used manual data analysis and basic software like MS Excel. Institutions use many disconnected in-house systems for different activities without intelligent tools or data warehouses to support data analytics. The results indicate deficiency in activities that promote data analytics and data-driven decision-making, such as capacity-building training and leadership support within institutions.

Drawing on the need to shift from competency-based information literacy instruction to critical information literacy instruction, this study examines scholarly communication and scholarly misconduct in the health sciences based on literary evidence. We report on a qualitative systematic review of forms of scholarly misconduct in the health sciences and participants' attitudes towards these phenomena, with the goal of developing new, critical, approaches to information literacy instruction. The data synthesis process indicates there are four major areas of misconduct in health science research: in conducting research, publishing, following research protocols and determining authority. This categorization informs a framework for critical information literacy dispositions and pedagogies for researchers across health disciplines and geographies.

The availability of large scholarly full-text datasets with in-text citations annotated opens the opportunity to investigate how articles have been cited in scientific literature at scale. However, duplicate documents may exist in a dataset, and these duplicates may impact downstream analysis such as calculating citation counts. Document conflation is the task of identifying documents that are nearly identical to each other. This study evaluates document conflation in the Semantic Scholar Open Research Corpus (S2ORC), a dataset containing over 12 million scholarly articles. The evaluation was based on 6,099,232 full-text S2ORC documents with PubMed IDs (PMIDs) or PubMed Central IDs (PMCIDs). Our findings showed that a portion of S2ORC might contain duplicates. Of the 6,099,232 full-text documents, 1,280,196 (20.99%) had the same PMIDs or PMCIDs as at least one other document. Pairwise comparisons of their full text found that at least 9.44% of the documents in S2ORC had duplicates.

This is the first on-going project presenting a four-dimension metric to identify scholars’ emotional intelligence (EI), which has yet to receive much attention. Current study proposes a data-driven metric rather than a subjective survey to reflect EI’s first dimension, self-awareness. By employing paired-T-tests on the DBLP dataset, we found that highly self-aware scholars are more likely to strive to improve with higher stability, leading to higher productivity and impact. Meanwhile, they have a more significant number of higher diverse collaborators. This research highlights the importance of one’s self-awareness to his/her scientific performance.

This poster problematizes selected presentations and outcomes of an IFLA satellite conference on the history of professional organizations of librarians and information scientists. Representatives from organizations, including ASIST and library associations around the world from four continents (Europe, North America, Asia, Africa) will meet at the Mundaneum in Mons, Belgium. The theme of the conference is “Preserving our origins: Approaches to the organization, curation, and historiography of the record of national and international organizations in libraries, information, and documentation.”

An irony within information science is that its many professional associations often fail to identify and preserve their own history. Their work is documented, but often not well preserved, and hidden from a wider audience. Born digital materials are especially vulnerable to loss (Neal, 2015). This poster identifies problems and best practices in managing the documents of historical value of volunteer-led associations and invites ASIST members to contribute to the historical work of the association.

The goal is to identify common problems in preserving association histories. The poster will present several projects discussed at the IFLA satellite conference, as well as proposed ways forward that will advance historical work for the information professions.

This research analyzes the results of a study that is part of a larger, interdisciplinary, and multi-institutional project that examines the usability and effectiveness of virtual reality (VR) training for library and information science (LIS) graduate students and professionals in gaining skills for interacting effectively with patrons in crisis. This paper reports on key findings related to the effectiveness of VR training for teaching empathy, confidence, and de-escalation skills for LIS graduate students. The findings illustrate that VR has the potential to impact LIS graduate education by reaching a wider audience that introduces training in low-stakes, immersive environments and that does not pose harm to patrons in crisis. This study also contributes innovative approaches that support training in skills including empathy, confidence, and de-escalation.

Until recently, research on information behavior and practices has focused on a series of actions, including having information needs, seeking information to satisfy those needs, and using the acquired information to varying degrees. However, in digital environments with an enormous distribution of information, it is necessary to consider information practices by focusing on their relationship with negative behaviors, such as information avoidance. Based on the discourses by Japanese Canadian seniors on information behavior during the COVID-19 pandemic, this study attempted to construct an expanded model of everyday information practices (EIP) that incorporates the concept of information avoidance into the EIP model proposed by Savolainen. Findings suggest that information avoidance is likely related to an individual's social context and that, as a result of information avoidance, different means of information acquisition are chosen from a person’s stock of knowledge, resulting in different aspects of the individual's information practices.

Much work has been carried out to highlight the accessibility challenges of blind programmers. Yet, relatively little has been known about how blind programmers help each other to solve problems. We present a data-driven approach to explore collaborative problem-solving of users in the Program-l community of, by, and for blind programmers. We collected 8,344 longitudinal email threads from 778 users from 2004 through 2022 to observe the dynamics of collaborative problem-solving among blind programmers. Our embedding-based topic modeling and assortativity network analysis reveal that the knowledge of blind programmers diverges between when asking and answering questions. Our findings also suggest that users who have a high cluster level in the first year of activity and members are more likely to interact with other members with different roles. Our paper contributes to the field of social computing by introducing the first large-scale study of a unique community of blind programmers.

Today, while one sector can effortlessly gain access to data provided by another, there may also be instances of injustice between data producers and decision-makers. This study applies Heeks and Shekhar's framework of data justice to analyze two humanities and citizen science projects in Taiwan: the collection of old photos by the Taipei Public Library and the citizen archivist project of the National Archives Administration. The study explores data justice issues within libraries and archives and examines the applicability of the data justice framework to citizen humanities projects. The research findings indicate that although there are unequal power dynamics between institutions and citizen scientists regarding data, the selected cases involve relatively straightforward task contexts and individuals, resulting in minimal violation of data rights for citizen scientists. Consequently, these two projects have limited observable instances of data injustice.

This poster reports on the preliminary findings of a phenomenological investigation of humanities researchers’ perceptions of “data” to develop workable strategies for future collection and curation of research materials used for humanities research. An archival approach is proposed to curate individual humanities researchers’ research materials.

As advancements in artificial intelligence (AI) technology have opened new avenues for artistic expression and creation, human-AI collaboration in creative activities has garnered increasing attention. Through semi-structured interviews with 15 participants, we investigate the intrinsic and extrinsic factors that motivate laypeople’s engagement with AI painting, as well as challenges and concerns faced by users. Our findings reveal that laypeople engage with AI painting for emotional needs like entertainment, aesthetics, surprise, and curiosity, personal utilitarian needs such as self-expression and customization, and social interaction through sharing and communication. Despite the appeal of novelty and unpredictability they also encountered challenges related to technical and system functionality, personal and environmental factors, as well as concerns about algorithm bias, pornography misuse, employment risks, copyright disputes and ethical implications. Findings provide preliminary evidence of the potential and limitations of AI in democratizing creative activities, and offer implications for designing and developing of AI assistance tools.

This poster describes the research design to study the information behaviours of individuals with diverse sexual orientation and gender identities (SOGI) and how they negotiate between their personal privacy and their social and public identities in the digital world. In Australia, there is no legal right to privacy, and our increasing reliance on new technologies has created legal and regulatory challenges, especially for vulnerable populations such as persons with diverse SOGI. The poster will outline the literature review, research questions, aims, and methods guiding the research. Methods proposed for this study include an analysis of legal frameworks and policies, a survey of approximately 100 people with diverse SOGI about their use of digital social technologies, and in-depth interviews with a sub-set of self-selected survey participants. Information behaviour theories and privacy literacy theories will be used to understand participants' behaviours and experiences regarding how they use digital social media, and the practices they have in place to manage their privacy.

A paradigm shift is currently underway with the emergence of the Collections as Data movement, which advocates the creation and dissemination of cultural heritage collections that are amenable to large-scale computation to empower both collection managers and users. Although this discourse is beginning to gain some traction in the literature, critical evidence-based assessments of the opportunities and risks of this process are underexplored. This paper presents the results of a content analysis of the official position statements (n=83) produced in the Collections as Data forums and written by international professionals working with digital collections. Although preliminary, the analysis presented and discussed here sheds light on the initial reception of the idea of Collections as Data and its articulation in practice. The study represents the first systematic attempt to explore the complexities of the intersection between ethics and artificial intelligence in the context of cultural heritage, aiming at providing a valuable precedent for further elaboration and discussion.

This study investigated how digital human can help libraries promote reading. This study recruited 103 participants for a user experiment to explore their satisfaction with reading promotional videos using digital humans. At the same time, the effect of using digital human in library reading promotion was analyzed from the perspective of whether users are willing to share and interact. Research has found that user satisfaction with them is not high due to digital human presence in their voice and intonation. But users are more interested in using anime digital human videos. This discovery can help libraries better carry out reading promotion and digital services.

This study aims to investigate the topics regarding disabled people in library and information science research areas through systematic content analysis, with the intention to profile the critical issue of accessibility and its development in LIS research and practices. 330 research papers in LISA published during 2011-2021 were collected and analyzed to understand the distribution of the research topics, subjects, methodologies, and findings about disabled people. The results showed an extended period of user-oriented research concerns yet very little exploration of service framework issues of policies, legislation, and technical services, in actual practices. Based on the results, specific and necessary research topics and issues were identified for future studies.

This study explores the impact of the COVID-19 pandemic on informal online learning behavior through the lens of online video tutorials hosted on YouTube. Over 40,000 YouTube comments were collected for topic modeling analysis and uncovered thirteen latent topics related to three learning behavior types, that is, environment structuring, help-seeking, and self-evaluation. These learning behaviors underwent varying degrees of change after the pandemic declaration and normalized after that, highlighting the importance of adapting teaching methods to meet the changing needs of learners in response to any learning disruptions.

This study uses an exploratory, sequential mixed method design to explore how Kuwaiti students share academic information using social media. Phase I involved in-depth, semi-structured interviews with 12 undergraduates; in Phase II, 683 students completed online questionnaires.

Extensive use of social media, especially WhatsApp groups, was discovered, and the main patterns were providing information, exchanging information, and non-sharing. Before sharing, students evaluated information accuracy, provided instructions, and reviewed information with others.

Motivations for sharing were enjoyment in helping others, mutual interest, enhancing one’s reputation, and reciprocity; reasons for non-sharing included self-doubt, fear of information being used for cheating, and competition. Class news, summaries, class notes and materials were shared for group projects, written assignments, presentations, and exams. Online leering used during COVID-19 accelerated sharing behaviors, and older students shared more actively and in more diverse patterns.

A new conceptual model was drawn to explain these complex behaviors, and recommendations were provided for supporting them.

Most workers’ activities in the workplace can be classed as knowledge work (KW). Previous research has described workers’ tasks very broadly, making it difficult to know the actual work undertaken. We hypothesise that deconstructing what goes on inside a task could reveal many subtasks and activities at levels that are, as yet, unexamined and in need of deeper understanding. To explore this further, a data set collected from research fellows who described a specific knowledge work task was examined. The task was broken down into many subtasks and activities, showing that one single task contains many levels of tasks. This level of understanding may allow for the development of tools to support the worker.

It is a common phenomenon for Tang poems to cite the allusions, which can generate a rich relationship network. However, insufficient attention has been paid to investigating the relationship network. To address the research gap, by employing the theories and methods of information science, this study presents a method of combining fine grained co-citation relationship and semantic features to link allusion words. We constructed a fine-grained co-citation network between the allusion words by adding the cited positions and sentiments. We then transformed the fine-grained weights into relational similarities. Moreover, we also leveraged the explanatory texts as semantic information of each allusion word to map the semantic embedding vectors and calculated the similarities as the semantic similarities. Finally, we applied the link prediction algorithm to implement the allusion word linking. Our experimental results reveal that adding the cited positions and sentiments as well as semantic similarities can improve the performance in the task of allusion word linking and achieve 0.869 on AUC score. Additionally, we explore the linking results from the perspective of the shortest path and find some regular knowledge. Overall, our study extends the application scope of information science and promotes the development of Chinese traditional cultural resources.

This poster captures the initiatives of African community and public libraries in the early stages of COVID-19. This is part of an ongoing research on the impact of the COVID-19 and long-term implications for African libraries. We conducted 11 interviews in 9 countries using both purposive and snowball sampling. We analyzed the recorded data through a thematic analysis approach. Arising from the data, we found that librarians employed empathetic information interventions using widely available information technology tools to deliver critical information to promote the well-being of their communities. The libraries initiated social cohesion talks to foster unity among families. They also used WhatsApp to send snapshots of study materials to examination candidates and to point people to social services. This work contributes data to scant literature about the initiatives of African community and public libraries during the COVID-19 and how low resource libraries can support their communities in crisis.

In academic Q&A, question cues are characteristics of natural language for responders to understand askers’ needs. However, knowledge is limited concerning responders’ answerability evaluations which could be influenced by question cues (e.g., emotional expressiveness, complexity) and topic types. To address the gap, this research conducted an experiment to investigate how different question cues and topic types influence evaluations of question answerability. Sentiment analyses were conducted to assess responders’ evaluations of question answerability. Results showed that responders favored answering complex questions rather than simple ones. Responders also held more divergent opinions regarding whether to answer STEM questions with different cues while holding a more inclusive answerability evaluation of non-STEM questions.

This study collects heterogeneous information from multiple sources related to the Grand Canal, a cultural route in China, to construct a multilingual ontology of the Grand Canal. The research designs a data translation, optimization, and multilingual ontology construction method to reveal the internal and external characteristics and associations of the Grand Canal, which can provide users with multilingual and ordered knowledge of the cultural route, providing a feasible solution for digital preservation and global sharing of the cultural routes. On this basis, a multilingual knowledge intermediary platform is built.

The Nineteenth-Century Short Title Catalog (NSTC) attempts to provide comprehensive coverage of the print record in the English-speaking world from 1801 to 1918 based primarily on the catalog records of Britain’s five legal deposit libraries during the 19th century. Previous studies have used the NSTC to quantitatively study broad trends in Victorian-era British publishing, but they have not sufficiently acknowledged the NSTC’s limitations. This study works within the NSTC’s limitations by using it to quantitatively study the phenomena of legal deposit. Analysis of two, 90-volume random samples of first edition English novels reveals the impact of changing library acquisition practices and legal deposit legislation on the comprehensiveness of Britain’s legal deposit libraries, as well as the NSTC’s comprehensiveness and how it can be utilized for quantitative book history despite its deficiencies.

Existing models of the work of analytics teams and how they impact health organizations do not describe the activities, nor their relationship to the organization, in sufficient detail for optimal decision making. This poster reports on an ongoing study of the work of healthcare and population health analytics teams to rectify this gap. We are interviewing members of analytics teams in healthcare and population health organizations to gather data on the processes, deliverables, downstream and upstream stakeholders, and artifacts that the teams rely on to conduct and manage their work. The primary analysis uses a grounded theory approach, with a secondary activity theory analysis of relationships between the analytics team, identified cognitive artifacts, and intended outcomes. In this poster, we intend to present the methods, participants, and some preliminary observations. We hope that this presentation will solicit feedback that leads to improvements in the study.

This study determined that 18.6% of 199 recipients of Sloan Research Fellowships in Mathematics between 1955 to 1979 did not change institutions during the first 40 years of their academic careers after obtaining a doctor of philosophy degree. The results indicated no significant difference in the average productivity per researcher when they were categorized by their number of home institutions. However, average productivity differed significantly between recipients with and without international mobility. The results indicated an increasing trend in annual average productivity for all groups of researchers with international mobility except for researchers with one home institution. The inconsistent findings for the four main groups merit further investigation of the relationships between the number of home institutions, international mobility status, and productivity.

This poster presents public library innovations during the Covid-19 pandemic. Many public libraries quickly adapted to the pandemic environment, changing and improving their operations and services to meet the new challenges and demands from their users. We collected two datasets to investigate these innovations: the first dataset comprised 751 tweets from the 12 largest public libraries in the U.S., and the second dataset included 72 articles from 3 major professional magazines. These datasets were analyzed to identify innovative services provided between 2020 and 2021. A rigorous content analysis involving multiple coders was conducted. The findings from both datasets highlight that public libraries quickly changed their service delivery modes and implemented diverse innovative services to bridge the digital divide, support health and technology literacy, and help with unemployment and career development. Libraries made efforts to reach out to their communities during lockdowns and also served as community education centers during difficult times, combating with misinformation and focusing on assisting marginalized populations. Additionally, the magazine articles introduced innovative services that pertained not only to user services but also to internal operations within public libraries.

Examining the factors influencing the decision of Library and Information Science (LIS) professionals to leave their positions, we conducted a survival analysis using the WILIS2 dataset, with a specific focus on professionals of color. In our study, we investigated the impact of the comfort level with newer technology and the perceived effectiveness of online courses during their graduate program on professionals' retention decisions, while also examining potential differential effects across racial groups. The results revealed that both the comfort level with newer technology and the perceived effectiveness of online courses were significantly positively associated with professionals' decision to stay in their positions. Importantly, we found no significant differential effects of these factors across different racial groups. These findings provide empirical support for promoting a comfortable level of technological proficiency and recognizing the value of online education as effective strategies to improve retention rates among LIS professionals, particularly those from minoritized backgrounds. Additionally, we discussed the implications of our study for Equity, Diversity, Justice, and Inclusion (EDJI) in the field, and offered suggestions for fostering an inclusive and supportive environment based on our findings.

This study employs a scientometric approach to shed light on the evolving intellectual structure of AI in healthcare (AIH) research. The results substantiate the multi-layered nature of knowledge production within the AIH domain, comprising the foundation, technology, and application layers. The application layer has witnessed a notable expansion in both its scope and depth, encompassing diverse areas including medical image analysis, data analysis and mining, decision support systems, and intelligence assistance. Additionally, a significant shift has occurred in its knowledge production process, wherein the conventional reliance on empiricism has been augmented by the incorporation of datafied innovation. This process of datafication has enriched the empirical underpinnings of AIH research, fostering a more comprehensive and evidence-based approach to knowledge production.

We are a team of citizen science volunteers and academics presenting new information about how long-serving Old Weather project volunteers left the leading citizen science platform, Zooniverse.org, and created their own opensource transcription tool to capture meteorological data from historic ship logbooks, for climate science. The value of a hierarchical model of crowdsourcing, whereby an organization creates a project and invites volunteers to perform defined tasks, is well-established (Brabham, 2013; Estellés-Arolas and González-Ladrón-de-Guevara, 2012), as is the value of co-productive models between organizations and volunteers (Hedges and Dunn, 2018; Ridge et al, 2021). Information about how crowdsourcing projects evolve from hierarchical to co-productive models, and how volunteers create tools as part of such a transformation, is less well-known. We present how OW volunteers created a tool for high-quality data collection, and implications for citizen science collaborations.

The outbreak of COVID-19 has resulted in an increase in health misinformation spreading on social media, emphasizing the need for effective science communication to combat this issue. This study aimed to analyze the relationship between misinformation spreading and science communication network. We identified misinformation spreaders, scientists, and laypeople from COVID vaccine-related tweets, and we carried out network analysis to examine the ingroup and intergroup interactions. We found that individuals in all three groups tended to interact with people who were dissimilar to them. Additionally, we found that misinformation spreading, and science communication network are polarized. Finally, suggestions were provided to achieve higher engagement of science communication.

The Result Assessment Tool (RAT) is a software toolkit for conducting research using results from commercial search engines and other information retrieval (IR) systems. This software combines modules used for the design and management of studies, the automatic collection of search results through web scraping, and the assessment of search results by jurors using different scales in an assessment interface. Due to the flexibility of RAT, several types of studies can be implemented, for example, classification studies and qualitative content analyses in addition to classic retrieval tests. Therefore, RAT is a versatile tool and useful in various disciplines.

The following poster reports the preliminary results of a comparison between a 2017 survey on health information and the same survey administered in 2023. The primary research question is: How did the COVID-19 pandemic impact undergraduate students’ judgement of credibility in health information sources? Recent research has shown that student health information seeking has changed around the COVID-19 pandemic. However, the research has not noted whether the pandemic has had a lasting impact on credibility of sources during health information seeking at the presumptive tail end of the pandemic in 2023. The original study in 2017 surveyed the undergraduate population of McGill University. The same survey was readministered in 2023, with COVID-19 specific questions added. The preliminary analysis suggests that the COVID-19 pandemic impacted students’ judgement of credibility in health information sources. There were negative changes in the perceived credibility of family/friends, well-known websites, wiki, blogs/forums, and social media for both everyday life health and COVID-19 information from 2017 to 2023. Conversely, government/university, scholarly books/journals, and TV/radio all saw increases in perceived credibility for both everyday life health and COVID-19 information.

Drawing upon the communication privacy management theory, this study proposes a moderated mediation model to examine the links between privacy perceptions and user engagement in mobile health applications (mHealth apps). Through an online survey involving 1149 mHealth app users in China, results showed that social privacy concerns were negatively related to user engagement in mHealth apps, and perceived privacy of the app partially mediated this relationship. Moreover, perceived control positively moderated the indirect relationship between social privacy concerns and user engagement via perceived privacy. Finally, theoretical and practical implications are discussed.

This research investigates individualized information literacy instruction (ILI) for different student populations in higher education and its impact on engagement and learning outcomes (SLOs), specifically first-year students of color. The study is designed as a QUAL+quan convergent mixed-methods study, and will utilize critical race theory as its theoretical framework, as well as a participatory action approach. It is designed as a multi-session IL workshop series delivered outside of the traditional classroom, and is comprised of six one-hour sessions: an initial focus group, four IL sessions focusing on specific aspects of the research process, and semi-structured interviews. Data collected through discussions, open-ended worksheets with rubrics, and pre- and post-surveys will be analyzed to measure whether the instructional series impacted SLOs. The study is significant as it’s the first to specifically address the systemic racial achievement gap coupled with a multi-session IL workshop series, and can serve as a model for other institutions.

Data governance policy is of great importance for the development of digital economy. This paper proposes a four-dimensional policy mining framework to analyze the 1097 central data governance policies. We depict the development trajectory, analyze the policy subjects involved into the policy-making, trace the sources of policy, and characterize the policy tool structure based on the policy coding. We find that the core departments of data governance policy making need to strengthen the awareness of data governance; Policy-making should source enough convincing and authoritative policies to strengthen the long-term effectiveness and impact policies; There are imbalances and deficiencies of policy tool structure. It provides reference for other countries in data governance.