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.