Preliminary Conference Agenda

[目的/意义]本文旨在分析其中各主体的收益、成本和风险问题，为有针对性地改善科学数据共享中存在的问题。[方法/过程]基于演化博弈论和主体间性理论，分析数据生产者、数据使用者和数据管理平台在科学数据共享中的角色、关联及利弊得失，构建了三方的演化博弈模型，并对各主体的演化稳定策略和系统的演化均衡点进行分析与数值仿真。[结果/结论]各主体的科学数据共享策略受到其科研收益、激励收益的正向影响和共享成本、共享风险的负向影响，且对不同因素的敏感性不同。最后，从研究结果和实际情况入手，为推动科学数据共享进程提出对策建议。

摘 要：[目的/意义]跨学科科研合作是现今科学研究的重要方式，对跨学科科研合作影响因素进行研究对合作的开展有着积极的推动作用。通过对跨学科科研合作相关原始研究进行深入分析，丰富了跨学科科研合作影响因素的研究成果，为相关研究的深入进行提供理论参考，促进跨学科科研合作更好地开展。[方法/过程]本研究通过采用元人种志这一定性研究方法，对筛选出的17项原始研究按照严格的研究步骤进行综合集成，构建了跨学科科研合作影响因素模型。[结果/结论]跨学科科研合作受个体特质、外部环境、学科背景和内部合作因素的影响。其中个体特质因素包括：个人认知、合作意愿、学习工作经历和个体感知子维度；外部环境因素包括政策支持、科研资助、合作平台、时间投入和学科体制支持子维度；学科背景因素包括学科差异和研究范式子维度；内部合作因素包括沟通交流、关系质量、资源共享、科研产出、有力的领导和认知一致性子维度。研究结果对于后续跨学科科研合作影响因素的研究有一定的启示作用。

【目的】现有的影响力评价方法没有考虑到学者在领域科研创新中发挥的不同角色，可能忽略顶尖科学家以外的其他学者对科研创新的贡献，本文旨在为学者的学术影响力评价提供一个更为全面、细致的方法，促进对学者成果更为公正、准确的认知。【方法】本文结合创新扩散理论，提出了一种文献科研创新角色划分方法，并根据学者和文献的对应关系将角色映射至学者层面，旨在分析和评估学者在其研究领域内的角色定位与影响力。【结果】以区块链领域学者为例进行实证分析，本研究提出的学者影响力评价方法与传统评价相比，可以多阶段地反映学者的实际贡献，同时可以刻画学者研究轨迹与学术影响力的变化。

The use of big data and other technical means to comprehensively recognize the multiple values of academic achievements has become an important goal of the evaluation of academic achievements in China in recent years, and Academic Knowledge Mapping, as an increasingly mature semantic organization technology, provides new practical ideas for the realization of this goal. In this study, firstly, under the guidance of the framework of the whole evaluation system, we sort out the information organization needs in different dimensions of the evaluation research scenarios, and theoretically analyze the feasibility of applying academic knowledge mapping to the evaluation of achievements, secondly, under the guidance of the scenario-based information needs, we put forward the framework of constructing academic knowledge mapping for the evaluation scenarios of academic achievements respectively from the content and technical levels, and finally, we put forward the targeted mapping construction framework for the evaluation scenarios of academic achievements combined with the reform background of the academic evaluation in China at present. Finally, we propose a targeted mapping construction strategy in the context of the current reform of China's academic evaluation, with a view to providing reference for the development and implementation of China's academic achievement evaluation support tools, providing more comprehensive and accurate data support for the development of academic achievement evaluation, and promoting the reform and development of China's academic achievement evaluation work from the perspective of information organization.

[研究目标] 科研工作与审稿活动之间的关系是了解学者“科研-审稿”行为模式、洞察同行评议系统运行机制的关键。本文旨在探索学者科研工作与审稿活动之间的权衡模式，回答学者的科研工作与审稿活动到底是“精力竞争”还是“审研相长”的研究问题。[研究方法] 我们随机获得了179737名全球性、全学科学者的审稿与文献计量数据，构建了科研工作对审稿活动的固定效应模型。[结论和讨论] 学者的科研工作和审稿活动彼此强化。微观上，学者们的审稿与科研是“审研相长”而非“精力竞争”。学者积极参与审稿活动并不会挤占用于科研工作的精力。学界应积极鼓励学者“科研-审稿”积极地互动。宏观上，这种特性也表明同行评议的供需有自发地走向平衡而非失衡的态势，“审研相长”是支持同行评议系统可持续性的一个证据，也证实了同行评议危机也并非由“精力竞争”引起。不断深化学者们的科研工作与审稿活动的依赖关系，是学者生涯发展、同行评议系统繁荣的方向。

Virtual reality (VR) content creation, as a maker activity, involves individuals in creating an immersive environment to present multimedia information, fostering their information and digital literacy skills. In recent years, low tech-barrier approaches have democratized VR creation to novice users, attracting attention in schools, GLAMs, and community centers. Synergizing the interdisciplinary, theory-driven, and practice-oriented expertise of the organizers, this workshop aims to 1) promote “iMakeVR”, a low tech-barrier pedagogical approach and tools to VR maker activity, 2) discuss its applications in participants’ areas of expertise, and 3) formulate research and teaching agendas in the information field. This workshop will comprise an introduction to relevant key concepts and examples, sharing on related projects, a hands-on tutorial, and a group discussion. Expected outcomes include a collection of interdisciplinary research topics, proposed pedagogical applications, and plans for future research and community-based collaboration.

Mental health has been drawing significant attention in recent years. An individual with mental illness may experience ups and downs during everyday activities, and feel depressed and anxious, or more severe conditions like schizophrenia and bipolar disorder, which may lead to disrupt behavior or inability to carry out necessary activities as expected. The fast development of emerging technologies made it possible to utilize artificial intelligence (AI) in mental health research and clinical practice for the purpose of raising the awareness, and improving the diagnosis, treatment, and support for people with mental health issues or concerns. The workshop will provide a forum for researchers and practitioners to discuss key issues and lessons learned on AI and mental health. This multidisciplinary workshop will facilitate interactive discussions on the implications of these challenges from a variety of disciplines and perspectives in an international context.

The rapid advancement of Artificial Intelligence (AI) has ushered in significant opportunities while also giving rise to profound ethical concerns. Governments, non-governmental organizations, research institutions, and industries worldwide have fervently engaged in the exploration and implementation of AI ethics. This work reviews the research and practice of AI ethical governance issues worldwide, with a particular emphasis on AI ethics legislation and the practical application of ethical principles. Within the current landscape of AI ethics governance research, several pressing challenges emerge, including the establishment of a robust ethical decision-making framework, the integration of ethical principles into AI systems, and the formulation of guiding legal policies. Based on an extensive analysis of existing AI ethics governance theories and technological research, this study presents an innovative conceptual framework rooted in dynamic feedback reinforcement learning theory. This pioneering framework, cultivated through the collaboration of multiple stakeholders, encompasses various dimensions encompassing law, technology, and market dynamics. Additionally, it establishes an AI ethics governance committee tasked with supervising the behavior of AI systems, guiding their acquisition of ethical principles, and adapting to the ever-evolving environment. The overarching objective of this collaborative, multi-faceted AI ethics governance framework is to serve as a reference point for global AI ethics governance mechanisms and to promote the sustainable development of the AI industry. By taking into account legal, technological, and market factors, our aim is to facilitate a harmonious interaction between technology, humanity, and society, ultimately paving the way for a healthy and inclusive intelligent society.

The swift advancement of Large Language Models (LLMs) and their associated applications has ushered in a new era of convenience, but it also harbors the risks of misuse, such as academic cheating. To mitigate such risks, AI-generated text detectors have been widely adopted in educational and academic scenarios. However, their effectiveness and robustness in diverse scenarios are questionable. Increasingly sophisticated evasion methods are being developed to circumvent these detectors, creating an ongoing contest between detection and evasion. While the detectability of AI-generated text has begun to attract significant interest from the research community, little has been done to evaluate the impact of user-based prompt engineering on detectors' performance. This paper focuses on the evasion of detection methods based on prompt engineering from the perspective of general users by changing the writing style of LLM-generated text. Our findings reveal that by simply altering prompts, state-of-the-art detectors can be easily evaded with F-1 dropping over 50\%, highlighting their vulnerability. We believe that the issue of AI-generated text detection remains an unresolved challenge. As LLMs become increasingly powerful and humans become more proficient in using them, it is even less likely to detect AI text in the future.

Abstract. This study investigates the causal relationship between affiliation diver-sity and paper disruptiveness in the field of Artificial Intelligence (AI). We ob-tained 646,100 AI-related papers with complete affiliation information between 1950 and 2019 from the Microsoft Academic Graph. Descriptive analysis and Propensity Score Matching (PSM) methods are employed in this study. The results show that homophily (over 70%) has still been prevalent over the past 70 years among multi-affiliation collaborations in AI, despite the average affiliation diver-sity exhibiting startling upward trends when AI steps into the deep learning stage. Affiliation diversity cannot promote the disruptiveness of AI papers. On the con-trary, AI papers with affiliation diversity can be 1.75% less disruptive compared to AI papers that collaborated by similar affiliations.

摘要：[目的/意义]命名实体识别是自然语言处理任务中众多下游任务的重要基础步骤。古籍作为中华文明的载体，在众多方面具有独特性，为了提高古籍文本中实体识别的准确率，助力古籍资源的智能利用和开发。[方法/过程]首先，本文选取课题组精加工的《二十四史》作为原始数据集，使用gujibert_fan预训练模型对SEQ、SEQ_CRF、SPANPRED模型进行训练，对古籍文本中的实体进行预测。其次，使用了强化实体识别的方法，将预测的实体分为正确和错误两类，生成预测数据集，旨在提供更多上下文和语境信息，以提高实体识别性能。最后，本文引入了合并和多数投票机制的方法（MU）与预测数据集进行整合并训练，以便更准确地捕捉实体边界和类型。[结果/结论]通过评估指标来验证模型的效果，实验结果表明，合并方法的加入使得实体识别的召回率有显著提升，多数投票机制方法提升了模型的F1值。

Based on socio-technical system theory, this study aims to unravel the interaction effect of technical attributes (offering explainable artificial intelligence, XAI) and social factors (algorithm aversion, trust propensity) on human trust to AI and AI-assisted decision-making performance. This study takes a lab experiments approach in the scenario of cross-border e-commerce product selection task. A cross-border e-commerce product selection experiment platform was designed, and 78 candidates were recruited. Configuration analysis was conducted based on the final 69 valid data sets. The findings indicate that, while offering XAI, the behavioral trust to AI can be improved;no matter the trust propensity being high or low, while they don’t have algorithm aversion perception, the AI-assisted decision-making performance can also be improved.

[Purpose/Significance]Network social organizations is an emerging force that cannot be ignored in the evolution of network public opinion. The research on the problem of cooperative refuting rumors led by government and supplemented by network social organization and the public is of enlightening significance for deepening the mechanism of network cooperative refuting rumors under emergencies.[Methods/Procedures]Based on the evolutionary game theory, this paper constructs a dynamic model of bounded rational network social organizations and the public, analyzes the evolutionary stability strategies and evolutionary paths of both sides of the game under different government supervision, and uses Matlab software combined with novel coronavirus(COVID-19) to simulate the situation [Results/Conclusion]The results show that the initial willingness of the players has an impact on the strategy selection, and appropriate rewards and punishments, reducing the misjudgment rate of information review, strengthening the supervision of online public opinion situation and reducing the extra cost of high-quality rumor information can effectively promote the online social organizations to publish high quality rumor information; Guiding by government policies, cultivating public information literacy, strengthening supervision, distinguishing rewards and punishments, and optimizing the government information audit mechanism are conducive to giving full play to the synergy between online social organizations and the public, and improving the efficiency of government network rumor dispelling under emergencies.

[目的/意义] 为考察数据协同情境中社交媒体知识共享的影响因素及组态特征，缓解数据协同主体间存在的知识不平等障碍，以推动数据协同顺利运行和数据价值有效开发。 [方法/过程]本文融合协同技术采纳模型和技术信任理论，综合采用定量和定性比较分析方法，构建并检验了数据协同情境中社交媒体知识共享影响因素的理论模型。[结果/结论]量化分析表明，价值效果预期、使用成本预期和社群环境影响对知识共享意愿产生影响，知识共享意愿、技术信任及应用便利条件影响知识共享行为。同时，任务技术匹配和技术使用经验均对价值效果预期有正向预测作用，技术使用经验对使用成本有负向预测作用。定性比较分析发现，产生强知识共享行为的原因可归纳为参与者驱动型、技术信任-交互便利推动型和低成本驱动型，共三种主要类型。

用户信息素养在谣言验证过程中扮演着至关重要的角色，对于社交媒体谣言治理和网络空间净化具有重要意义。然而，不同信息素养能力的组合如何协同、动态地促进谣言验证意愿，仍缺乏相关的理论研究。本文采用组态视角，通过进行一项两期的纵向研究调查，旨在探索用户在短期和长期时间框架下实现高谣言验证意愿的前因条件组态，并比较它们之间的差异。通过模糊集定性比较分析（fsQCA）的数据分析，本研究发现在短期时，高谣言验证意愿存在三种不同的条件组态：核心技能型、规范应用型和批判反思型，而在长期时，只存在一种条件组态：用户主导与环境辅助型。本研究为信息素养与谣言验证相关研究提供了新的视角和方法启示，也为信息素养教育与社交媒体谣言治理提供了重要的参考依据。

The engagement of LIS stakeholders is critical as local contexts around the world all have unique expectations and infrastructures that guide the quality and development of LIS education for professional practice.The workshop will discuss all potential competencies of the engagement of LIS stakeholders while undertaking the new IFLA Guidelines for Professional Library and Information Science (LIS) Education Programmes. It is also important to consider similarities and differences with other LIS education standards/guidelines. After a brief presentation of the Guidelines and it's competencies defined, there will be small group discussions on the Guidelines’ definition of LIS and Foundational Knowledge Areas and their application, concluding with a critical response on translating ideas into action. All LIS stakeholders are welcomed.

In an era where data reuse is increasingly central to research efficacy, this study delves into the granularity of data-centric work tasks and addresses task goals, the challenges researchers encounter (i.e., the gaps), and the essential resources for these tasks. Utilizing a systematic literature review, we articulate a classification framework that identifies four distinct goal families and twelve goal categories. Within the goal families of “Research” and “Data”, goals are further characterized as either exploratory, confirmatory, or balanced. Our results demonstrate that the nature of goals has implications for how researchers anticipate gaps and resources. Specifically, those with more defined (confirmatory and balanced) goals predict the hurdles they will face and are proactive in identifying resources, whereas those with exploratory goals show less foresight in challenges but seek a wider range of potential resources. This study enhances our understanding of the complex interplay among goals, gaps, and resources in data-centric research tasks, offering avenues for more targeted research support services.

Improving algorithmic literacy empowers people to engage with algorithm-driven products across myriad applications with material impact. However, there remains a shortage of interventions aimed at nurturing algorithmic lit-eracy within everyday life. With algorithmic awareness and algorithmic knowledge serving as vital pillars for algorithmic literacy, we initiated a heuristic intervention. This intervention drew upon official information from platforms including Taobao, Bilibili, and Weibo, integrating stimulus questions and heuristic materials. We conducted online experiments, amassing a dataset of 622 responses. The outcomes of our data analysis substantiated the efficacy of the heuristic intervention in bolstering algo-rithmic awareness and advancing algorithmic knowledge. Moreover, the re-sults indicated a positive correlation between users' algorithmic knowledge and their algorithmic awareness, suggesting that as users' algorithmic knowledge increased, so did their level of algorithmic awareness. These findings hold substantial significance for guiding future practices in the cultivation of algorithmic literacy.

Based on the privacy calculus model, users traditionally made decisions about privacy disclosure by weighing perceived values against privacy concerns. However, recent studies indicate that users’ cognitive style and trust also play significant roles in a social media context. This study examines how these factors collectively influence users’ privacy disclosure behavior from a configurational perspective. Through an online survey, we collected data from 452 respondents on a Chinese social media platform. The results reveal that users’ decision-making on privacy disclosure is a complex process with various configurations. For individuals with a field-dependent cognitive style, cognitive style is more important than the trade-off between perceived values and privacy concerns. On the other hand, for field-independent individuals, decisions are not only influenced by the trade-off between perceived values and privacy concerns but also by trust. We finally discuss the theoretical and practical implications of these findings.

档案、图书等文献资源因在跨时空传播历程中不断地被解构与重构而动态地、多重地参与人类记忆构建。本文借助记忆理论，深度解析文献资源参与记忆建构的层次、维度、系统，进而设计包含面向个体记忆解构、面向社会记忆重塑、面向文化记忆凝聚三个渐进、共生的文献资源语义组织模型，并结合文献单元的结构与特征，逐层阐释信息采集加工、数据关联发现、知识聚合服务的语义组织实现过程。在此基础上，选取青岛早期工人运动文献进行实证研究，论证语义内容集成、语义内外关联、语义多维增值的步骤，探索面向记忆建构的文献资源语义组织具体实现路径。

随着元宇宙、AIGC等新兴技术的发展与应用，文化遗产资源创新活化的内容与形式得到了积极的拓展与丰富，文化资源馆藏机构需要在新型服务模式下重构资源开放保障措施，以更好应对不同场景下资源开放与利用的服务需求。本文遵循扎根理论研究方法的指导原则和研究过程，对12家海外博物馆的政策文本进行深入分析后提炼出博物馆数字资源开放框架，从利益相关者、权利与责任以及适用范围入手考察了博物馆数字资源开放获取的政策构成，使用其他12家海外代表性博物馆进行理论饱和度检验，通过对24家海外博物馆的政策分析发现博物馆重视开放授权环节，但质量较低、数字化不完善等因素限制了开放效果的提升与优化空间。研究针对传统服务模式下GLAM机构的数字资源开放获取过程搭建了参与主体与开放因素框架，并对博物馆文化遗产资源开放的现实状况进行了分析阐释，为文化遗产资源活化利用提供参考与借鉴。

The development of archival documentary heritage is related to the realization of its own historical, cultural and social values. This study follows a logical sequence from theoretical analysis to empirical research. In the theoretical analysis section, we analyze the motivation for the development of digital narratives of archival documentary heritage from the aspects of policy, technology and media. In the empirical research section, after researching the current situation of the development of archival documentary heritage in China, we found that most archival institutions have the problems of a single narrative approach, lack of narrative technology, unsound narrative standards, and lack of publicity and promotion in the process of developing digital narrativization of archival documentary heritage. Subsequently, this study proposes four optimization strategies for the development of archival documentary heritage, namely, optimization of narrative mode, synthesis of narrative tools, unification of narrative standard, and enhancement of publicity and promotion, and constructs a narrative framework for the development of archival documentary heritage based on the digital narrative theory, which explains the objectives of the organization of archival information, mining of archival content, and visualization of results in the process of the development of archival documentary heritage, and provides a clear explanation for the development of digital narrative. The framework explains the organization of archival information, the mining of archival content and the visualization of results in the process of archival documentary heritage development, and provides feasible suggestions for the application of digital narrative to archival documentary heritage development.

【目的／意义】发展我国网络档案信息资源建设，构建统一行动框架是推进数字中国建设战略的重要举措之一。【方法／过程】基于全球代表性实践展开调查与分析，梳理不同类型的网络档案信息资源建设实践及其要素，使用文本分析方法构建总结性行动框架，后基于要点、问题讨论我国行动框架的更优设计。【结果／结论】框架内容依托建设主体、建设对象、建设过程、建设成果、建设保障五大要素及其相互关系来呈现。我国实践可在遵循框架核心要点的基础之上，面向不足展开更优设计。核心要点包括：加强多元主体协作；完整识别对象的主题和来源属性；以开发基础设施级数据服务为建设导向。同时，也需结合不足与最新理论、实践从发展广泛多元的实践基础，紧跟技术与社会演变升级策略，变革建设思想与理论三个方面升级框架设计。

This study seeks to unravel the connotations and influencing factors of nonprofit utilization of digital resources of ancient books in preservation institutions, laying a solid foundation for enhancing the efficacy of ancient book digital resource services. Interviews were conducted with three sets of audiences: the providers of ancient book digital resources, the users, and policy makers. Integrating the grounded coding results, the 3E theory, and the MOA framework, we defined the essence of nonprofit utilization of digital resources of ancient books and established a theoretical framework for its influencing factors. We discovered that the concept can be decomposed into three dimensions: economy, effectiveness and efficiency. The influencing factors' theoretical framework covers eight dimensions, namely digital platform, database resources, staff, management, funding, technology, users, and macro-environment. This encompasses 20 main categories such as data structure, institutional design, and staff philosophy, involving three mechanisms of operation: drivers, opportunities, and abilities.

To prevent other types of mental health problems from being misclassified as depression, as well as to remedy the problem of inadequate resources for mental health consultations. This study first analyzes the types of different causes of mental health problems, providing an important basis for better understanding the diversity and complexity of this field. Subsequently, a machine learning approach was used to predict the potential causes of dif-ferent types of mental health problems. This research provides new perspec-tives and methods for early identification and personalized treatment of mental health problems. The experimental results show that depression ac-counts for only 16.9% of mental health problems. In the prediction of the causes of mental health problems, the SVM method performed best in pre-dicting the causes of mental health problems, outperforming 5 machine learning methods and 3 deep learning methods. Through these studies, we hope to prevent other types of mental health problems from being misclassi-fied as depression and to remedy the lack of resources for mental health counseling. This will help increase the success rate of early intervention and provide better mental health support for patients.

This paper describes the developing process and preliminary testing of a physical representation system aimed at supporting preschool children's picture book search. The authors first conducted named entity recognition (NER) on a corpus of 880 picture book summaries to supplement a metadata schema identified in prior research. They then designed a system using colored stripes and icons to physically represent these metadata elements on book spines. A small-scale experiment (N=8) comparing search performance between children taught the representation system versus untaught controls was conducted. The results suggest that the representation system can be understood by children and improves recall, precision, and success rates. The findings provide initial evidence that mapping metadata to intuitive physical identifiers could enhance young children's book search experiences and engagement. Further research with larger samples is needed to evaluate the effectiveness of this approach.

Abstract. Billions of scientific papers lead to the need to identify essential parts from the massive text. Scientific research is an activity from putting forward problems to using methods. To learn the main idea from scientific papers, we fo-cus on extracting problem and method sentences. Annotating sentences in scien-tific papers is labor-intensive, resulting in the creation of small-scale datasets that limit model learning. To tackle this challenge, data augmentation has been adopted due to its ability to generate synthetic data with minor variations, thereby expand-ing the scale of the original training dataset. Nowadays, there are various data augmentation methods, such as those based on random word replacement or back translation. Nevertheless, their suitability for sentence classification tasks in sci-entific papers remains unexplored. Thus, this paper constructs two manually an-notation datasets and evaluates their performance. Furthermore, this paper delves into the mechanisms underlying their effects. Previous studies have suggested that data augmentation can diminish reliance on high-frequency patterns in mod-els. Therefore, this paper employs attention values to represent the model's de-pendence on words, and analyzes how data augmentation methods alter the atten-tion values of individual words within sentences. The experimental results indi-cate that data augmentation methods can improve the macro F1 score in sentence classification tasks. Furthermore, data augmentation methods effectively reduce the attention values assigned to stop words, commonly used words in scientific papers, and commonly used words in method and problem sentences.

Aiming at the limitation of single associations in knowledge unit citation network, this paper enhances the diversity of semantic association types between network nodes through citation function, thereby proposing a citation function-aware knowledge unit citation network and conducting multi-dimensional analysis of domain knowledge. Firstly, the academic text is parsed to extract information such as knowledge units, citation links, citation context and citation objects, and identify their citation functions. On this basis, the complex network method is used to construct a citation function aware-knowledge unit citation network, and the multidimensional analysis of domain knowledge is carried out from the analysis of citation network structure, multidimensional citation relationship analysis of knowledge units, and citation function-aware knowledge community analysis. The proceedings of ACL (Association for Computational Linguistics) are used as an example for empirical research. The results verify the effectiveness of the proposed method; discover the usage, extension and comparison patterns among domain knowledge; and enrich the semantic information of knowledge communities. This study extends the research method of knowledge unit citation network, deeply revealing the semantic relationships between discipline knowledge, and providing a new path for analyzing discipline knowledge structure.

在学科界限日益模糊、学科壁垒逐渐被打破的背景之下，运用不同学科方法、理论来解决更加综合、复杂的学术问题成为了越来越多学者的研究模式。为识别此类跨学科知识交流活动中不同学者扮演角色的重要程度、不同学者间进行跨学科知识交流行为的趋势大小和跨学科知识交流网络的演化规律，本文构建起生态学与物理学交叉视角下的跨学科知识交流动力模型。首先将物理学中势能的相关概念和生态学中生态位的相关概念移植至跨学科知识交流领域，并论证其可行性与必要性；其次以知识主体的知识存量为基础，融合知识主体所处网络结构特征、知识主体的知识生态位宽度计算知识主体的跨学科知识交流势能；然后以知识主体间的跨学科知识交流势能差为基础，结合主体间的知识生态位重叠度，量化知识主体间的跨学科知识交流趋势，构建起知识主体视角的跨学科知识交流动力模型；接着以所有的知识主体所处的知识交流网络为研究对象，描述整个学术生态系统中跨学科知识交流行为的活跃程度和网络的演化进程，构建起跨学科知识交流网络视角下的网络演化动力模型；最后，以health informatics领域下学者的跨学科知识交流网络为实证数据，验证了知识主体视角的跨学科知识交流动力模型，分析了网络视角的跨学科知识交流演化进程和规律。

In the constantly evolving landscape of information and innovation, the ability to accurately predict emerging topics is vital across various fields. This study regards weak signals as the early stages of emerging topics and identifies these nascent topics through an innovative three-dimensional analysis framework for weak signals. Initially, we construct a keyword citation network, identifying novel signal collections based on changes in network structure. We then conduct a temporal analysis of the visibility and diffusion of signals with time-weighted attributes to identify weak signals, consider their social influence, and quantify the influence of weak signals from the public perception perspective using alternative metrics. Finally, an evaluation framework for emerging topics based on weak signals is built across three dimensions: visibility, diffusion, and influence. We applied the method to gene editing and underwent multi-angle method verification and comparative analysis. The results demonstrate that this approach not only enhances the capability to identify emerging topics at an early stage and grasp their development trends but also enables the identification of emerging topics with significant social impact, thereby offering valuable insights for strategic decision-making, innovation management, and future foresight.

【目的/意义】针对研究人员的研究兴趣呈阶段性变化，提出了一种基于注意传播机制和时序注意机制的科技论文推荐模型，以提升论文推荐的效果。【方法/过程】结合用户行为和论文属性特征构建协同知识图谱，利用TransR方法将图谱中的节点映射到向量空间中；基于注意传播机制从协同知识图谱中学习论文的知识增强特征表示，基于时序注意机制从阅读行为中学习研究人员的动态偏好表示，计算研究人员与论文的匹配分数得到推荐列表。【结果/结论】以“区块链实验室”数据进行大量的实验分析，结果表明所提出模型的推荐表现最佳，可以说明基于注意传播机制和时序注意机制的表征方法能够更为精准地捕获用户的动态偏好，且适用于论文推荐系统中。【创新/局限】通过融合用户行为和论文知识图谱，利用基于注意传播机制和时序注意机制的表征方法实现了科技论文动态推荐。虽然通过融合辅助信息的方式挖掘用户的兴趣演变，但辅助信息的类型数量有待提升。

汉字是我国的非物质文化遗产，其字形在数千年的传承中几经变化。要探究字形演变对汉字系统宏观规律的影响，可从汉字的字形复杂度特征入手，对其在汉字演变过程之中分布规律的变化进行探索。本文提出像素法——一种测量字形复杂度的新方法，由此测量、比较了汉字字形复杂度及其动链在不同字库与文本中的统计特征。研究发现，字形复杂度在汉字系统、汉语文本中的分布规律，以及字形复杂度动链的频次分布、长度分布规律，均没有因字形演变而改变，汉字字形复杂度统计特征的稳定性是其构形系统与汉语词长共同演化的结果。

Chinese characters are intangible cultural heritage in China, and their forms have undergone several changes over thousands of years of inheritance. To explore the impact of the evolution of Chinese characters on their usage patterns, we can start with the glyph complexity characteristics of Chinese characters and explore the changes in their distribution patterns during the evolution process. This article proposes the pixel method - a new method for measuring the complexity of glyphs, which measures and compares the statistical characteristics of Chinese character glyph complexity and its motifs in different word libraries and texts. Research has found that the distribution pattern of glyph complexity in Chinese character systems and texts, as well as the frequency and length distribution patterns of glyph complexity motifs, have not changed due to the evolution of glyphs. The stability of the statistical characteristics of glyph complexity in Chinese characters is the result of the co-evolution of their structural system and Chinese word length.

Gender inequality in the information technology (IT) industry varies geographically, especially between the Global North and South. While cultural biases, school prejudices, and workplace norms contribute to gender disparities in the North, the South faces limitations in education, socioeconomics, and deeply ingrained gender roles. These disparities, influenced by socio-cultural and economic factors, demand tailored solutions. Achieving global gender equality in IT necessitates an understanding of these nuanced distinctions. This panel offers an overview of IT gender disparity and explores key themes: the perspectives of developed nations in the Northern Hemisphere and developing nations in the Global South, the challenges women in the South encounter in Science, Technology, Engineering, and Math (STEM) education and IT opportunities, the gender divide in digital publishing (in production and consumption), and the impact of government policies and educational initiatives in the North on promoting gender equality in IT.

The purpose of this study is to explore the main methods to improve the ability of college students to distinguish between true and false health information by using different educational media. The research adopts the method of experimental research and designs three groups of experiments, namely poster, text and video. Each group conducted two rounds of experiments: pre-test and post-test. 110 college students were recruited to participate in the experiment, and the experimental results were analyzed by means of paired samples t-test, independent samples t-test and ANOVA. It is found that all three educational media have significantly improved college students' ability to identify health information, especially posters, which have the greatest impact on the improvement of health information identification ability. At the same time, factors such as attention to health information and questioning have significantly affected the improvement of college students' ability to identify health information. The research results have theoretical and practical significance on how to carry out health information literacy education for college students and how to improve their health information literacy.

This article presents a systematic literature review of the impact of AI writing as-sistant tools on education over the past decade. Utilizing keyword searches in Scopus and Web of Science databases, we initially identified 1191 relevant doc-uments, ultimately narrowing it down to 112 for analysis. To discern prevailing research trends, these documents were categorized by geography, impact on writ-ing and research direction (Automated Writing Evaluation (AWE), Automated Writing Feedback (AWCF), and Automated text generation (ATG)). The results indicate that research in this field reached its peak in 2022-2023, marked by a sudden increase in ATG, particularly concerning ChatGPT. Leading scholars in the AI-writing fields are predominantly based in the United States and China. In terms of educational impact, the overall sentiment is positive, particularly with re-spect to the use of AWE and AWCF, which have proven effective in enhancing writing quality and efficiency. Notably, among all types of AI assistants, ATG has raised most ethical concerns, including issues related to data bias, privacy, and academic integrity. Future research should consider examining the impact of AI assistants on writing in non-academic contexts and addressing urgent issues surrounding the use of ATG in education.

In recent years, Web-based Augmented Reality (WebAR) has emerged as a transformative technology with significant implications for the preservation, interpretation, and dissemination of cultural heritage. This paper investigated the embodied interaction with digital cultural heritage utilizing WebAR technology, with the iMDC Vision Project in Inner Mongolia as a case study site. We conducted semi-structured interviews with both project developers and digital cultural collection users. The thematic analysis approach is used to guide the qualitative data analysis process. Based on the thematic analysis of data, this study proposes an integrated framework for an embodied interactive experience of cultural heritage through WebAR.

This study compared the publishing preferences of two main groups of prolific authors from the fields of philosophy and literature for the years 2001– 2020. Specifically, the top 100 prolific authors of philosophy journal articles, literature journal articles, philosophy book chapters, and literature book chapters were identified and analyzed. The results indicated that the prolific authors of works related to philosophy and literature tended not to engage in co-authorship. The prolific article authors from both fields primarily published articles in journals that corresponded to their respective fields and were affiliated with institutes that corresponded to their respective fields. The prolific book chapter authors (PBCAs) primarily published book chapters; however, most of the PBCAs from the field of philosophy were affiliated with non-philosophy university departments, whereas most of the PBCAs from the field of literature were affiliated with literature-related university departments. This finding indicates that prolific authors from different fields of humanities exhibit different preferences with re- spect to publication format. Some humanities researchers do not place a high value on journal articles. Because a considerable number of prolific authors also published books, our results suggest that focusing on only journal articles is in- adequate for exploring the characteristics of humanities publications. Therefore, future studies should investigate why these authors are highly productive even though they do not exhibit a preference for co-authorship.

Understanding public opinion on social media is vital for developing evidence-based interventions that drive positive change in global Covid-19 vaccination efforts. This ongoing study thus addresses the public opinion shifting on Twitter through the analysis of over 200k users engaged in online discussions related to the Covid-19 vaccine. We then operationalize opinion shifting in three dimensions: word 1-grams shifting, topic shifting and sentiment shifting. Day-scale usage frequency distributions for 1-grams are compared by rank turbulence divergence and the Mann-Kendall Mutation analysis is applied to determine the discussion shift based on the divergence score per two days. Text analysis methods are employed for topic clustering and sentiment analysis on tweets. Based on analyzing users' topic shifting, we explore opinion shifting from their sentiment changes on a topic. Our findings indicate that more than half of the social media users experience vaccine-related topic shifting. Users who experience topic shifting and who focus on a single topic have slightly different patterns of sentiment shifting when they hold the same sentiment on a topic. Individuals who focus on a single topic tend to experience increased sentiment polarization toward a particular sentiment direction over time.

Digital humanities (DH) is an evolving field fostering interdisciplinary col-laboration, with an impressive engagement of library and information sci-ence (LIS). To assess how DH is situated in the academic landscape and bridge the gap between DH and LIS, it is essential to undertake a comprehen-sive global investigation of the DH community, thereby gaining insights into its current identity. In this study, we examined the scholar composition of 129 digital humanities centers (DHCs) under a disciplinary classification scheme, and further explored the characteristics of the global DH community using methods of diversity measures and community detection. Our findings reveal that beyond traditional humanities, a diverse range of disciplines con-tributes to DH research and practice. Particularly noteworthy are the roles played by LIS and computer science, serving as two main sources of compu-tational expertise. Based on the congruence of community identities, it is im-perative for LIS, who has established a prominent presence within the DH field, to proactively connect to and embrace DH as a vital source of growth in its own domain.

International migrants face difficulties obtaining information for a quality life and well-being in the host country. Prior research indicates that international migrants often seek information from their co-national cohort or contacts from the same country. The downside of this practice, however, is that people can end up clustering in a small-world environment, hindering the information seekers' social adaptation in the long run. In the current research, we investigated the ongoing practices and future opportunities to connect international migrants with others beyond their co-national contacts. Our work zooms in on the providers' perspectives, which complements previous studies that pay exclusive attention to the information seekers. Specifically, we conducted in-depth interviews with 21 participants assisting the needs of informational migrants in the United States. Some of these people are fellow migrants from a different home country than the information seeker, whereas the rest are domestic residents. Our data revealed how these participants dealt with language barriers, overcame knowledge disparities, and calibrated their effort commitment as information providers. Based on these findings, we discuss directions for future information and communication technologies (ICT) design that can facilitate international migrants' daily information seeking by accounting for the provider's needs and concerns.

The advancement of information technology has significantly expanded the avenues and approaches through which individuals can access health-related information. However, this proliferation of information sources has also introduced challenges. Specifically, the abundance of diverse sources often generates conflicting health information, making it increasingly challenging for individuals to make informed decisions and navigate their health-related choices. While previous research has primarily focused on factors influencing adoption behavior, willingness to adopt, and the outcomes of adoption, there has been a relative neglect of the underlying processes, formation mechanisms, and the mechanics governing adoption behavior itself. To address this gap, this study takes a patient-centered approach to construct a model of Conflicting health information adoption. It seeks to comprehensively explore the adoption process and behavioral phenomena among patients, while also employing the concept of "trans-theory" to conduct a thorough analysis. The study's findings illuminate a common pattern when patients encounter conflicting health information: a misalignment between their cognitive understanding and their subsequent behavior across various facets of adoption behavior. This inconsistency arises from a complex interplay of factors, including the influence of information, individual abilities, psychological factors, and environmental conditions, all shaping the process of adopting Conflicting health information. This research, grounded in a patient-centric perspective, aims to shed light on the intricate dynamics of conflicting health information adoption. By bridging the gap between cognition and behavior, it seeks to contribute to a more informed and effective approach to health information management in a world marked by information abundance and complexity.

This study investigates the application of framing theory in the context of media coverage of the monkeypox outbreak, analyzing the divergent approaches adopted by mainstream and social media. Utilizing Hallahan’s (1999) framing models, the research methodically examined a corpus of 100 media items, comprising news articles and social media posts, collected from January to December 2022. The content was categorized under four principal themes: symptoms and transmission, prevention and treatment, public opinion and attitudes, and social strategies.

The analysis elucidated a notable differentiation in framing preferences. Mainstream media predominantly utilized 'Framing of Situations' and 'Framing of Attributes,' denoting a tendency towards narrative and descriptive reporting. In contrast, social media exhibited a proclivity for 'Framing of Actions' and 'Framing of Issues,' reflective of an interactive and dynamic communicative style. A Chi-square statistical analysis yielded a significant association between the types of media and the employed framing models (Chi-square = 25.38, p < 0.0001). The computed Cramér's V value of 0.316 indicated a moderate degree of association, suggesting nuanced variances in framing strategies between mainstream and social media.

This study contributes to the scholarly understanding of media framing in the milieu of a global health crisis. The findings underscore the distinct ways in which different media platforms engage audiences and shape public perceptions during health emergencies. These insights are instrumental for the formulation of effective public health communication strategies, highlighting the pivotal role of media framing in the dissemination and reception of health-related information.

The development of a large-scale pre-trained model has opened a new stage for artificial intelligence. More and more researchers have introduced models such as BERT and GPT into various application fields. However, the problems of complexity and interpretability have posed a degree of risk to the model’s generalization. Meanwhile, the accuracy of the model directly depends on the quality of the training dataset. Optimizing the dataset often leads to better results. Therefore, this article proposes the GSBERT model. Firstly, the model can explore a decision basis of the BERT model by gradient saliency. It can also present the BERT model decision-making process by visualization, so as to makes model decisions interpretable. Secondly, based on the interpretability of the model, the sample feature token is extracted to form a feature word list. Then classify and predict samples by Naive Bayes model again. Lastly, to test the effectiveness of GSBERT in analyzing data consistency, the GSBERT model is used to automatically detect and re-label the misclassified samples of the news classification datasets. The accuracy of model judgment also improved by 12.55% on the validation set due to the improved quality of the dataset. Not only can this study guide dataset quality detection in pre-trained models, but also can improves the accuracy of the model by peering at each layer during the running status of the BERT model. This method aims to provide reference ideas for the wider application of deep learning models in various fields.

随着网络社交媒体的普及，人们现实中的交往关系可能被复制到网络中，同时基于趣缘进一步延伸，形成网络圈层。网络圈层中的用户在海量信息的包裹下筑茧于无形，但圈层带来的情感共鸣和归属感可能导致用户不愿打破信息茧房。本文以豆瓣小组用户为样本，首先通过深度访谈和开放编码，探索社交媒体用户信息茧房滞留行为的表现特征。根据质性分析结果构建信息茧房滞留行为影响因素模型，收集问卷数据检验假设，使用fsQCA从组态视角进行补充分析。研究得出，社交媒体用户信息茧房滞留的行为的表现特征多种多样，影响该行为的因素包括信息技术、圈层建设、绩效期望、享乐动机、使用习惯、信息同质和信息过载，社交媒体用户信息茧房滞留行为成因路径较为复杂。研究结果有助于了解网络圈层中社交媒体用户应对信息茧房的方式，为社交媒体用户行为引导和网络环境治理提供一定的参考。

[目的/意义]公共数据开放是我国大数据发展过程中的首要任务，是为新时代赋能的重要研究领域之一。为此，本文回顾公共数据开放领域的研究现状，通过主题分析，发现热门研究主题，并进行热点主题的演化趋势分析与预测。[方法/过程]利用深度学习模型BERTopic进行主题识别，分析国内外公共数据开放研究的主题分类、关联和分布，并分别对国内外热度上升放缓的研究主题和持续攀升的研究主题进行梳理。[结果/结论]国内外均在“隐私保护”研究方面较为完善，国外的研究热点是“数据再利用”、“数据共享”和“数据质量”，国内的研究热点是“科学数据开放”、“数字经济”、“政府信息公开”、“政府开放数据平台”和“区块链”、“人工智能”等；相对国外热门研究主题，国内在“开放创新”、“公共数据再利用”、“数据质量”等几个领域还缺少关注；本研究认为，“公共数据开放需求”、“数据价值释放”、“建立统一数据市场”等研究领域将成为热点研究主题。

作为数字化时代的核心范畴和基本方法，算法为大型科技企业的全球规模和影响力持续注入动力，在赋能社会发展的同时也产生了一系列风险，因此需要深刻理解算法风险的治理规则，构建科学高效的算法治理机制。通过对美国算法治理实践中的联邦政策文本进行梳理，可以为社会治理中的算法治理提供一定参考借鉴。本文运用NVivo12 Plus质性文本分析工具进行编码，对美国联邦层面政策文件中关于算法治理的文本进行分析，从中提炼出由问责驱动的一系列治理路径，并试图梳理这些概念化的治理路径之间的联系，探讨其从概念走向实践过程中所面临的困难与挑战，最后总结概括得到在整个算法治理进程中算法问责呈现出的四大特征。

[目的/意义]在复杂国际形势背景下，产业危机情境和次生危机事件频发，我国产业正逐渐陷入外部不正当国际竞争导致的发展囹圄之中。产业危机事件发生后的应急恢复成为避免产业次生危机、保障产业永续健康发展的重要环节。因此，面向产业危机事件恢复的智慧情报服务成为产业危机事件恢复的重要辅助手段。[方法/过程]本文以“韧性治理”理论为研究视角，首先明确了产业危机事件恢复阶段的智慧情报服务需求，分析韧性治理理论与产业协同应急恢复智慧情报服务的适切性；然后，基于韧性治理理论，从韧性技术、韧性组织和韧性环境三个维度入手，构建产业协同应急恢复阶段的智慧情报服务模式；最后，以我国半导体产业为例，阐释该模式的运行流程，分析该模式的价值。[结果/结论]本文系统性地提出产业危机事件恢复阶段的智慧情报服务需求，构建产业协同应急恢复阶段的智慧情报服务模式，明确其运行流程，利用Vensim进行系统动力学仿真，验证该模式的有效性并进行敏感性分析，剖析该模式的实际价值，为我国产业面对危机事件的处置、恢复及治理及提供了智慧情报服务模式的支持，为产业链与创新链融合创造基础。

Globally concerns for first the digital divide and then the grey digital divide have been expressed over several decades. Rapid technological development and in particular the fourth industrial revolution (4IR), artificial intelligence (AI) and Society 5.0 is increasing concerns for older generations to fall behind in all contexts of life involving the use of technology ranging from mobile phones, digital information access and social media. The grey digital divide represents elderly people at retirement age and even younger (50+) who have access to technology but who struggle to master all the knowledge and skill sets to effectively benefit from digital opportunities. This panel will discuss the phenomena of the grey digital divide, reasons and urgency for concern (illustrated with examples from various contexts), types of divides, intergenerational approaches as solutions (scope, applications, examples), mutual benefits with intergenerational research, learning and support, and initiatives applicable in different contexts.

The theoretical framework of recordkeeping informatics was developed by Australasian archival scholars as a reconceptualisation of records management for the digital, networked environment. The purpose of this workshop is to introduce the framework and to consider its applicability in non-Western contexts by exploring the influence of Chinese cultural characteristics in organisational recordkeeping.

The 5th Workshop on Extraction and Evaluation of Knowledge Entities from Scientific Documents (EEKE 2024) and the 4th AI + Informetrics (AII2024) will be held in Changchun, China and online at iConference 2024. The goal of these two workshop series (https://eeke-workshop.github.io/ and https://ai-informetrics.github.io/) are to engage the related communities in open problems in the extraction and evaluation of knowledge entities from scientific documents and the modeling and applications of AI + Informetrics for broad interests in science of science, science, technology, & innovation (ST&I), etc. This joint workshop consists of keynotes, oral presentations, and posters, and will attract not only academic researchers and librarians but also decision makers from governments and practical sectors. The website for EEKE-AII 2024 is https://eeke-workshop.github.io/2024/ .

The use of learning analytics (LA), defined as personalised learning environments through the continuous measurement, collection, analysis and reporting of data about learners and their context, is rapidly expanding. A growing body of literature has questioned the benefits attributed to LA solutions and raised a number of concerns about the current developments in the education sector. However, we know little about how the beliefs, values and emotions of different groups of educational practitioners shape how they engage with learning analytics technologies and influence the evolution of the cultures of practice shaping the adoption of learning analytics. Here, we report on research that asks: how do culturally situated beliefs, values and emotions shape UK-based higher education practitioners’ engagements with learning analytics? We conducted 34 semi-structured interviews and four focus groups with staff from a provider of digital services for UK higher education and staff from UK universities. With insights from this research, we aim to contribute to empower practitioners in higher education and relevant stakeholders to foster the development of critical and reflective data cultures that are able to exploit the possibilities of learning analytics while being

critically responsive to their societal implications and limitations.

Digital inclusion research requires interdisciplinary collaboration to achieve a comprehensive understanding and develop effective strategies. This paper highlights the importance of interdisciplinary collaboration in digital inclusion research and provides a framework for understanding the contributions of different disciplines, using Library and Information Science, Computer Science, and Economics as examples. Specifically, it analyzes the relationship between these disciplines and digital inclusion, research topics related to digital inclusion, and research paradigms for studying digital inclusion. To address the three levels of the digital divide, the paper proposes an “Access - Capability - Outcome” three-layer relationship model to explain the functions of different disciplines in digital inclusion research. Additionally, two paths are proposed to promote integration and collaboration across different disciplines: the “inclusion technology for human” (technology-centered) path and the “inclusion of human in technology” (human-centered) path.

Abstract. Students with print disabilities frequently encounter the challenge of understanding alternative texts for complex, scholarly non-text contents. While a local solution exists, it is intellectually stimulating to explore this phenomenon more broadly in pertinent policies, standards, and academic literature. The process revealed that understandability appeared to be the most challenging barrier to in-formation accessibility despite being one of the widely accepted accessibility principles. For students with print disabilities to fully participate in equal educa-tion, the challenge of understandability, the last yard to information accessibility, must be dealt with. On the basis of local practices and literature analysis, this short paper presents reflections and suggestions. It is believed that collaboration between scholarly publishers, digital librarians, and universities, in addition to technologies, assistive and otherwise, is the most optimal solution.

Chronic disease prevention is crucial for maintaining national health and reducing medical burden. Transmission of disease prevention knowledge to people through human-AI collaboration is an emerging disruptive and revolutionary approach. Nonetheless, little research has been aimed at the knowledge adoption in different tasks under this scenario. This study explored the differences in knowledge adoption among task types in human-AI collaboration under the chronic disease prevention scenario. Twelve participants were recruited to complete the factual, interpretive, and exploratory tasks in human-AI collaboration. The subjective efficiency and effectiveness of knowledge adoption were obtained by questionnaires. The objective efficiency, including search time, switch frequency, and number of queries, was counted by Screen Recorder, while the objective effectiveness was scored by experts. Furthermore, non-parametric tests were used to compare the differences. The results showed that objective efficiency varied among different task types. Participants spent more time in the interpretive task and switched more pages in the exploratory task. Then, perceived effectiveness was the worst in the interpretive task. Finally, the participants got lower scores in the factual task and higher scores in the interpretive task. Therefore, suggestions for the means of human-AI collaboration have been proposed under the chronic disease scenario, including identifying scenarios to enhance user adaptation and immersion in completing different health tasks, enhancing the transparency and explainability of AI, especially in interpretive tasks, and adding references in the process of acquiring and understanding knowledge.

Improving the Q&A ability of government affairs dialogue robots (GDRs) has become an important issue. In practice, a large number of users with poor information literacy often pose vague questions, which makes it challenging for GDRs to comprehend their inquiries within a specific context. In order to enhance contextualization, this study has constructed a multi-turn dialogue model that incorporates R-GCN and fuzzy logic to base on the "question-answer-context" matching process. To obtain more accurate context, we propose a re-question mechanism to further press for contextual details. Additionally, we introduce the sub-graph matching mechanism of fuzzy logic and R-GCN to improve the accuracy of implicitly representation of Chinese logic in the contextualized matching process. This mechanism allows us to prune the context-irrelevant parts in the "answer", and obtain more complete context information. We collected over 300,000 words of real cases as the test-set. The results of the experiments show that this model can significantly improve the contextualized reasoning ability of GDRs in a more humanized way. The innovative response generation method in this research, which utilizes "question-answer-context" matching, is more suitable for complex scenarios where the user may not be articulate. It helps to lower the barrier for accessing government services and provides more user-friendly assistance to individuals with limited information literacy.

With the rapid advancement of artificial intelligence and natural language processing technologies, AI voice assistants are gaining attention for their potential to enable voice shopping. Based on cognitive appraisal theory, this study constructs a theoretical model of AI voice assistant user experience with emotional experience as a mediating variable, and examines the antecedent variables of emotional experience and the mechanism of its effect on users' willingness to adopt AI voice assistant in the context of voice shopping. The study used data collected from 318 users of AI voice assistants. Findings of Partial Least Squares (PLS-SEM) suggest that perceived service quality, and perceived entertainment significantly influence emotional state and emotional attachment towards the users' adoption intention. Perceived anthropomorphism significant influence the emotional attachment but not emotional state. By exploring the role of emotional experience in the user experience of AI voice assistants, this paper proposes suggestions to enhance user experience and promote voice shopping.

This study aims to investigate the effect of digital literacy on international students' adjustment to university life and how ICT self-efficacy mediates the relationship between digital literacy and adjustment to university life. A survey was conducted among 118 international undergraduate and graduate students in Seoul and Daegu. Analytical methods consisted of reliability analysis, validity analysis, regression analysis and multiple mediation analysis performed using the SPSS 26.0 and PROCESS macro (Version 4.0). The results indicated that digital literacy significantly affected international students' academic adjustment, social adjustment, personal-emotional adjustment, and institutional adjustment. In addition, ICT self-efficacy was identified as a mediating factor in the relationship between digital literacy and academic adjustment. These findings suggest that university libraries, which are the central institutions for providing academic information at universities, should provide a variety of digital literacy programs for international students to improve their ability to use digital tools and help them adjust to the SW-centered society and university life.

Information literacy plays a crucial role in empowering social media users to effectively engage in fact-checking behaviors, thereby shielding them from becoming victims of rumors and misinformation. However, existing research predominantly treats users as a homogeneous group, neglecting potentially distinct user profiles based on their levels of information literacy. Such profiles may exhibit significant variations in users’ fact-checking behaviors, and these differences could also change over time. To bridge this research gap, the present study adopted a person-centered approach and conducted a two-wave longitudinal investigation. Using latent profile analysis, this study identified three distinct latent profiles: the information literacy proficient profile, the information literacy competent profile, and the information literacy novice profile. Additionally, based on longitudinal data analysis, this study further revealed that, in the short term, the fact-checking behavior of the information literacy competent profile exhibits no significant difference compared to that of the information literacy proficient profile, and significantly outperforms the information literacy novice profile. However, in the long term, the fact-checking behavior of the information literacy competent profile and the information literacy novice profile no longer displays significant differences. This study offers valuable insights for social media platform managers to design targeted strategies tailored to distinct user profiles.

Cyber-attack incidents are increasing while there is limited understanding of cyber safety and security awareness amongst the migrant communities in Australia. Using a mixed methodology, we design a small-scale pilot study to understand the level of cyber security awareness among three migrant communities in Australia - Indonesia, Bangladesh and Somalia. One of our significant findings is that almost all the participants have experienced some level of cyber security and crime-related issues, while about 80% of the participants are not aware of how and where to report their incidents for legal action. Our focus-group discussion highlights participants' percep-tions, experiences and some socio-cultural and context-specific cybersecu-rity events, including cyberbullying and racism they face as migrants in Australia. Finally, we discussed how an enhanced understanding of culture and human context-specific factors could be helpful in designing and deliv-ering proper awareness-raising interventions for sustainable cyber security systems for migrant communities.

The contemporary learning landscape is profoundly shaped by technology, underscoring the importance of the ethical and responsible application of artificial intelligence (AI) tools in education. AI has the potential to revolutionize learning by tailoring instruction and administrative tasks. However, ethical considerations loom large in the integration of AI into education. Issues like data privacy and security and the presence of algorithmic bias present formidable challenges. To address these complexities, educational institutions must prioritize transparency, fairness, and inclusion when adopting AI technologies. Additionally, robust data governance and cybersecurity measures are essential. Educators and students must develop digital literacy and ethical awareness. By adhering to ethical standards, education stakeholders can harness the potential of AI while safeguarding the rights and well-being of learners and educators, ensuring that AI serves as an ethically driven force for educational advancement.

Virtual reality technology has brought about a new way of exercising through VR exergames. A critical and interesting question is whether users are willing to use VR exergames as an effective supplement to traditional exercise methods, or even as a complete substitute for some real sports. In this study, we aim to identify the factors that affect users' perception of VR exergames and intention to substitute them for real sports based on the Technology Readiness and Acceptance Model (TRAM). The proposed model and 16 hypotheses were tested by structural equation analysis using 248 validated questionnaires. Our results suggest that users' technological readiness and perceived interactivity significantly impact their perception of VR exergames and intention to substitute real sports. Perceived usefulness had a significant impact on substitution intention, while perceived ease of use not. Our findings provide important recommendations for future VR exergames development to enhance user experience and promote national fitness.

"Knowingly spreading rumors" has become an increasingly prevalent and seriously damaging phenomenon, yet existing research failed to provide enough theoretical explanations and empirical investigations of this issue. To bridge this research gap, this study utilizes the Q-methodology, which combines qualitative and quantitative approaches, to comprehensively explore the underlying core motivations and individual differences among social media users who knowingly spread rumors. By collecting, screening, and validating statements that encompass various reasons for knowingly spreading rumors on social media, we constructed a Q-set consisting of 44 statements. Subsequently, we invited 275 P-samples (in Q-methodology research, "P-samples" refer to the participants) to perform Q-sorting. Through factor analysis, we identified four types of motivations for knowingly spreading rumors on social media: Personal Involvement Type, Social Status Seeking Type, Entertainment Value Enhancement Type, and Uncertainty Reduction Type. Furthermore, our exploration delved into the unique relationships between these motivation types and demographic variables. The findings of this study not only broaden the boundaries of research within the realm of rumor spreading but also inspire further scholarly investigations into this under-examined behavior. This study also offers practical guidance for social media platforms, debunking organizations, and educational institutions, empowering them to develop effective strategies for preventing and intervening in the knowingly spread of rumors.

Smart cities take advantage of advances in ubiquitous computing and big data analytics to build and deploy technologies that increase efficiency and sustainability. However, benefits derived from smart cities are not equally distributed. In this paper, we consider how smart city initiatives can better serve and engage marginalized communities through a focus on the city of Baltimore, MD. Through focus groups with 43 Black Baltimore residents living in low-income neighborhoods, we identify key barriers they perceive to being more engaged in and trusting of smart city initiatives, as well as important disconnects between residents’ needs and the city’s solutions. Based on these findings, we make the case for cities to more deeply engage these communities in smart city initiatives, as many technologies are not designed with their unique needs in mind, and they are the most likely to experience harms from surveillance technologies that collect large quantities of data and build predictive models used by cities.

[Purpose/Significance] This paper aims to reveal the dynamic evolution trend of medical data sharing system and its influencing factors, so as to improve the research depth of medical data sharing and provide important basis and suggestions for medical data sharing.[Method/Process] According to the theory of planning behavior and social capital, the key factors affecting medical data sharing are explored, and the system dynamic model of medical data sharing among medical institutions is constructed, and the medical data sharing is simulated and predicted.[Results/Conclusions] The influence degree of various factors in the early stage of medical data sharing is as follows: organization sharing environment> sharing platform = data resources> institutional guarantee> individual willingness to share, and individual willingness to share grows fastest throughout the simulation cycle. Healthcare data sharing is more affected by the expected benefits than the shared costs in the early stage, and vice versa in the later stages. Laws affect healthcare data sharing more than policy and funding. In the later stage of medical data sharing, the lack of data resources and sharing platforms seriously reduced the level of medical data sharing.

[目的/意义]研究移动社交平台运动群体的健康信息行为，在提高个人健康信息认知素养的同时扩展研究的群体边界，为移动社交平台个性化推荐服务提供参考。[方法/过程]基于S-O-R理论构建研究的逻辑框架，利用大数据挖掘方法收集小红书上运动笔记文本数据，运用情感分类和K-means聚类研究方法分析用户健康信息行为的主题特征。[结果/结论]在S-O-R理论基础上，结合主题词构建主题特征模型，通过主题特征模型对用户的健康信息行为进行分析，为用户提高身体活动意识以及机构推出高质量健康信息服务提供研究依据。

Low willingness to accept green agricultural production slows down the restoration of ecological badlands. Effective advertising message interventions can improve farmers' attitudes. Eye-tracking methodology was used to explore the effects of message frames (positive vs. negative frames) and reference points (self-reference vs. others' reference) on farmers' willingness to accept green agricultural production and the mediating role of attention in it. A total of 81 microdata from the field experiment were generated. The results of the study showed that positively framed information was more attended to by farmers than negatively framed information, and that positively framed information had a greater facilitating effect on farmers' acceptance. There was no significant difference in attention between self-referential and others' reference point information. The two-way ANOVA of information frames and reference points on attention and acceptance indicated that people paid more attention and had stronger acceptance in the positive frames of others' reference points. Individual's attention time and number of gaze played a mediating role in the path of information frames, positively affecting farmers' willingness to accept. The results of this study provide guidance for the development of information dissemination strategies for the application of green agricultural production technologies in rural areas.

中医药知识中包含大量高度关联的数据，如何有效地组织这些复杂数据并挖掘其中隐藏的关系一直是亟待解决的问题。在本研究中，我们构建了TCMKS（Traditional Chinese Medicine Knowledge System）来管理、发现和利用中医药知识。系统包括10种实体类型（其中499种草药、452种植物、175,633种化合物、13,183种疾病、586,153种基因、1,907种miRNA、2,567条信号传导通路、9,693种症状、15,449种成分和46,160种方剂）、848,604个中西医学概念和222,396,210条关系，数据量超过了其他同类信息系统。TCMKS使用Neo4j存储数据并使用Django构建知识系统的前端交互界面。TCMKS支持中医药信息浏览、基于自然语言的智能查询、实体查询以及基于知识图谱的多跳关系查询等系统功能。TCMKS还将miRNA纳入其中，方便查询方剂-miRNA-疾病和草药-miRNA-疾病关系，有助于了解miRNA在疾病中的作用，促进开发涉及特定miRNA的新疗法。此外，本文对TCMKS进行了系统测评，其问答方法在评测中准确率达到了81.7%。通过对比TCMKS与通用生成式人工智能ChatGPT 3.5及领域生成式人工智能“数智岐黄”回答领域问题的效果，表明TCMKS具备更好的准确性和全面性。TCMKS在药物筛选、药理评估和药物开发方面的应用使其成为药用植物研究中不可或缺的资源。

兼具学习严肃性和游戏趣味性的教育游戏成为了一种有效的助学工具。人们的心流体验有助于增进学习效果、提升满意度。然而现有的教育游戏研究并未充分关注心流体验的作用，缺乏对心流前因和结果的深入探讨。为补充相关研究的空白，并为教育游戏的设计和开发提供实践指引，本研究采用实验法，要求61名参与者游玩《我的世界：教育版》这款教育游戏，在这一过程中观察记录参与者的游戏表现，并使用量表测量其他变量。回归分析结果支持了挑战-技能平衡与临场感对心流体验、心流体验对学习表现和学习满意度的正向影响，观察到自主定向和控制定向分别正向和负向调节了心流前因对心流体验的影响。本研究扩展了心流体验的相关研究，为教育游戏情境中心流体验的影响机制提供了实证依据，对于指导游戏开发人员创建更吸引人和有益于学习的游戏、提高学习者的表现和满意度也具有实践价值。

The power and expressiveness of AIs are rapidly increasing, and now AIs have the ability to complete tasks in crowdsourcing as if they were human crowd workers. Therefore, the development of methods to effectively aggregate the results of tasks performed by AIs and humans is becoming a critical problem. In this study, we revisit the Dawid-Skene model that has been used to aggregate human votes to obtain better results in classification problems. Most of the state-of-the-art AI classifiers predict the class probabilities as their output. Considering the probabilities represent their uncertainty, utilizing them in Dawid-Skene aggregation may provide higher-quality annotations. To this end, we introduce a variation of the Dawid-Skene model to directly use the probabilities without discarding them and conduct experiments with two real-world datasets of different domains. Experimental results show that the Dawid-Skene model with probabilities improves the overall accuracy. Moreover, a detailed analysis shows that the aggregation results were improved for classification tasks with high uncertainty.

Academic integrity is the foundation of quality higher education. However, the resources explaining the concept tend to be definition-driven, have complex language and sometimes even a severe tone to discourage students from breaking rules. This project deployed a gamified approach, designing and evaluating a 3D immersive video game for university students to facilitate their understanding and adoption of academic integrity principles. The game allowed students to be immersed in a virtual campus through an avatar and navigate a campus (e.g., garden, library, cafe, student accommodation) with scenario-based, academic integrity related dialogues with in-game characters. The paper aims to showcase the game design and student feedback of the game. Observation (e.g., eye-tracking) and interviews were conducted with 15 participants. Thematic analysis of the data shows that the game greatly enhanced student understanding of academic integrity concepts by providing contextualized memorability whilst relieving anxiety. Students’ engagement with the game was linked to game features such as appealing aesthetics, customization, and contextualization. The paper concludes by offering recommendations concerning the employment of gamification as an educational approach for instructing students in higher education on complex concepts, such as academic integrity.

The openness characteristic of social networks facilitates the rapid spread of rumors, necessitating effective methods for detecting and managing the abundance of rumors on social media. Existing studies have primarily focused on improving the accuracy of rumor detection, but often overlook the vital aspects of interpretability and explanation of rumor patterns, limiting their credibility and real-world usability. Additionally, previous works have typically examined only a subset of user features, content, and spreading structure, neglecting the analysis of compound rules. To address these limitations, we propose a novel framework for detecting rumor patterns that emphasize comprehensive feature construction and the explanation of compound rules. Our framework incorporates multi-dimensional features, including user characteristics, post content, and the structure of information propagation. Advanced techniques, including large language models (such as ChatGPT) and graph motif discovery algorithms, are employed for feature construction. By leveraging diverse features, crucial integrated rules identified by Rulefit can investigate the contextually dependent associations among various interrelated rumor factors. We consolidate and analyze seven distinct rumor patterns based on the Credible Early Detection Dataset, deriving valuable insights into the inherent characteristics of rumors. The recognition of rumor patterns empowers social media platforms and fact-checking organizations to develop targeted and explainable interventions that effectively mitigate the spread of rumors and safeguard the integrity of information. These interventions greatly enhance the transparency and trustworthiness of rumor management, fostering a more reliable information ecosystem.

The COVID-19 pandemic has evolved into a significant global public health crisis, exacerbated by the pervasive mis-/disinformation surrounding the coronavirus and vaccines. The ongoing crisis has underscored the urgent need for universal access to evidence-based health information on both childhood and adult immunization. Notably, advocating for community health and well-being has emerged as a focal point among iConference community members. Furthermore, the historic and ongoing decline in global immunization coverage necessitates user-centered health information support and interventions on a global scale. To foster inclusive and interactive discourse among the panelists and diverse audience, this non-traditional panel will delve into a wide range of subjects from current innovative LIS research to opportunities and challenges. It is expected to yield valuable theoretical and practical implications for the iConference community. The overarching objective is to cultivate best-practice approaches for LIS research, education, and practice, with a focus on community health and well-being.

This study comprehensively identifies existing and potential intellectual property intelligence service requirements through a combination of web-based surveys, literature reviews, and interviews. Building on this groundwork, it designs a questionnaire based on the Kano model to gauge the awareness and utilization of intellectual property intelligence services among researchers in the Guangdong-Hong Kong-Macao Greater Bay Area. Through two-dimensional attribute categorization, hybrid classification analysis, and Better-Worse Coefficients, the study classifies the attributes of IP service demands for researchers, and the TOPSIS comprehensive evaluation method is used to measure the demand importance of each service. Based on the analysis results, it is proposed that intellectual property intelligence services in the Guangdong-Hong Kong-Macao Greater Bay Area should be combined with the characteristics of different users to provide differentiated services, such as providing intellectual property intelligence services for researchers in academic and research institutions around the technical intelligence of scientific and technological theses, and providing intellectual property intelligence services for researchers in enterprises around the intelligence of patented technologies and market competition intelligence, and other strategies.

从海量专利中预测出高被引专利有利于最大程度发挥专利的技术价值，帮助挖掘高价值专利，是提升企业核心竞争力的关键。文章引入动态时间规整算法（DTW）来实现高被引专利的预测，首先从德温特数据库中获取集成电路领域的专利数据，其次构建高被引专利多维指标体系，然后对其进行数据预处理提取专利特征指标数据，最后使用DTW方法计算专利与高被引专利之间的相似度，用以识别高被引专利与一般专利。研究发现：集成电路领域专利与其高被引专利之间的DTW距离在0.358~1.512之间，前5%的阈值为0.604；影响集成电路领域高被引专利主要特征指标有：引用专利数、引用文献数、申请书页数以及授予国家数。研究证明将动态时间规整算法（DTW）用以实现高被引专利的预测的可行性以及有效性。

[研究目的]现代风险社会公共危机的表现形式及发展趋势呈现出不同程度的复杂性和不确定性，对于衍生舆情的治理也存在一定“迷雾空间”，因此为实现可持续化的治理管控需要关注契合现代化发展的舆情引导模式。[研究方法]基于文献研究梳理了现阶段国内外关于舆情治理提出的“参与式回应”互动范式和“法理式规制”策略模式；从情报感知视角剖析了风险环境下公共危机舆情治理对情报价值的刚性需求；从数据，情境和态势等层面挖掘情报知识对舆情引导的柔性辅助；从“理念— 方式—能力—引导”维度探析了“情报感知式精控引导策略”的赋能优势。[研究结论]在“情报感知—引导治理—价值涌现”的螺旋式循环过程中逐步还原“拟态社会”的事件真相，实现积极型治理主体的自我超越与智能化情报感知下的危机善治。

对文化遗产感官信息资源的管理和打造文化遗产的感官交互体验是数智时代文化遗产智慧化保护、开发与利用的关键路径。而相较于感官维度中的视觉 和听觉等模态，嗅觉维度是一个尚未被足够重视的维度。为实现文化遗产气味信息资源的活化与利用，论文对文化遗产中气味景观的信息揭示和数字营造展开探 索性的理论研究。首先是对气味景观、气味景观的信息揭示、气味景观的数字营造概念内涵进行界定。接着通过整合跨学科的理论，构建了以气味信息资源管理为中心的整体框架。最后面向文化遗产气味景观信息揭示和数字营造的系列问题进行了方法体系的梳理。对气味信息资源的智慧化管理是文化遗产活化的创新抓手，在文化遗产信息资源管理层面将丰富文化遗产的多维表达，在文化遗产数字交互层面将优化感官体验。

Social reading with "interaction" as its essential feature is flourishing, but does reading really require socialization? This study takes the "We Chat Reading" app as a representative of social reading platforms, we analyze the impact of different interaction modes of readers on their perceived value of social reading, in order to explore the value of social reading. Using a mixed research method combining experiments and questionnaires, we find that the perceived value of readers can be subdivided into four dimensions: perceived reading value,perceived emotional value,perceived social value and perceived functional value.The interactive approach driven by book reviews, abstracts, and interpersonal relationships brings higher perceived value.The perception of reading, social, and functional value by readers have a significant positive impact on reader satisfaction, and through satisfaction, it have a significant positive impact on readers' willingness to continue using. Among them, perceived functional value can also directly affect readers' willingness to continue using. Social reading provides readers with a new reading option, but its value varies from person to person, so to say that social reading is the "future of reading" is still too much to say. In practice, we suggest that social reading platforms promote the organic integration of reading and interaction, emphasize and improve the interactive functions with strong reader autonomy and flexibility, provide targeted services for readers, and thus achieve significant development in social reading.

The phenomenon of user knowledge withholding in virtual academic community is a key problem that destroys knowledge sharing and innovation and hinders the development of community. The paper divides the influencing factors of knowledge withholding from the two dimensions of enabling and inhibiting, and analyzes the multiple motivations of user knowledge withholding in virtual academic communities. A two-stage qualitative and quantitative hybrid research method based on csQCA and Decision Tree is proposed. The user data of three different disciplines virtual academic communities: Science and technology, Economics and management and Medicine are collected through a questionnaire. The result shows: (1) User knowledge withholding phenomenon in virtual academic communities is driven by the combination of enabling and disabling dimensions, and there are multiple configurations; (2) Knowledge power is the primary reason for user high knowledge withholding in online academic community; (3) The combination configuration of high knowledge withholding phenomenon in different disciplines virtual academic communities are different; The knowledge power, knowledge psychological ownership and knowledge withholding subjective norms of users in the science and technology community are playing a major role. The influence of knowledge power in the management community is more general and direct, while the influence of psychological ownership is less; The enabling dimension of medical community users often works together with the community reciprocity and privacy protection of the disabling dimension. (4) The research results of this hybrid research method can verify and supplement each other and provide more comprehensive conclusions.

[目的/意义]隐私关注是隐私研究的重要内容，因此有必要对社交网络用户隐私关注相关研究进行梳理，理清当前的研究进展，为未来的研究指明方向。[方法/过程]本研究采用文献调查与内容分析法对国内外社交网络用户隐私关注相关文献进行梳理，明晰了其发展历程，并借助可视化手段分析了国内外相关研究主题，总结出社交网络用户隐私关注研究现状。[结果/结论]通过文献梳理发现，目前有关社交网络用户隐私关注的文献研究方法与研究内容同质化程度较高。未来的研究方向有三点：结合社交网络用户客观数据、开发新的测量量表、拓展动态研究视角。

[目的/意义]近年来，社交网络用户隐私披露问题已成为国内外学者关注的热点话题之一。对社交网络用户隐私披露研究的国内外相关文献进行总结与述评，旨在揭示相关研究的最新成果，有利于研究者了解当前研究进展并在此基础上进行深入研究。[方法/过程]采用文献调研法，从理论基础、影响因素和影响后效3个方面分析社交网络用户隐私披露的研究现状，并提出研究不足和未来展望。[结果/结论]社交网络用户隐私披露的研究多为基于一种或结合多种理论探究用户隐私披露影响因素的研究。主要理论基础是隐私计算理论、沟通隐私管理理论和计划行为理论，影响因素多集中于个体因素而信息、平台和背景因素较少，影响后效主要关注对用户情感和心理、态度及行为的影响。未来研究可以拓宽研究范围，将更多理论引入社交网络用户隐私披露研究中，丰富其影响因素研究，更全面地理解用户的隐私披露；还可以进一步深入对社交网络用户隐私披露影响后效的研究。

[目的/意义]本研究旨在探究社交网络数据隐私风险的管理方法，力求从其他风险管理领域找到适用于社交网络数据隐私风险管理的方法模型。[方法/过程]明晰了社交网络数据隐私风险和金融风险的相关概念，通过类比分析法得出社交网络数据隐私风险与金融风险异同点。[结果/结论]本研究认为金融风险管理中的风险识别、风险评估及风险控制理论及模型都可以应用在社交网络数据隐私风险管理当中，并对其如何应用提供了思路。

[目的/意义]当前人脸识别技术应用场景日趋广泛，公众在不同场景下对接受人脸识别的存在差异化的利益-风险衡量。为此，基于社会交换理论，探究公众在政务和商业两种情境下接受人脸识别的影响因素，和是否具有用人脸信息换取便利的意愿，为人脸识别的场景化规制提供参考。[方法/过程]采用似不相关回归模型，确定个性、信任、利益、风险四个方面影响因素，并分析在不同情境下的差异。构建理论模型，提出研究假设，采用问卷调查法收集468份有效问卷，验证提出的研究假设。 [结果/结论] 确定了创新意愿、数字信任、便利感以及隐私风险对人脸识别接受意愿的作用关系。发现情境差异对于用户的决策过程具有显著影响：创新意愿在政务情境下提升接受意愿，在商业情境下无此作用；数字信任在政务情境下对接受意愿的促进作用比在商业情境下更显著；公众在商业情境下具有用“隐私换便利”的倾向；商业情境中的交换意愿可以促进政务情境下的接受意愿，反之不行。公众对于政务和商业两种情境下使用人脸识别的心理机制存在差异，有必要检视知情同意程序判断用户隐私态度的合理性，注意人脸识别技术的使用边界。

[目的/意义] 基于互联网技术的发展，以大数据、人工智能为代表的新一代技术和应用蓬勃发展，我国正式进入以数据为驱动的数智时代。依托互联网、大数据等技术开设的各种社交网络平台等给人们带来便利的同时，也存在着诸多安全问题，其中以社交用户的隐私保护问题广受国内外学者关注。[方法/过程] 本文系统梳理国内外社交网络用户隐私保护研究的文献，为该领域进一步深入研究提供借鉴和参考。采用统计分析法与内容分析法，对于社交网络用户隐私保护相关的研究，从隐私保护的诱因、隐私保护的手段到隐私保护的度量，进行较为系统的综述。［结果/结论］多学科交叉融合的社交网络数据隐私保护在数智时代的发展中扮演重要角色，要适应日渐复杂的应用环境和数字化、智能化的时代趋势，势必要构建技术、法律、第三方平台和用户四位一体的隐私保护体系。

Information ethics concerns ethical information access and use, social inclusion, cybersecurity alertness, etc. Apart from practical examples addressing these, discourse is often scholarly and philosophical with students (outside their study context) and ordinary citizens excluded. Scholarly discourse across disciplines (apart from Library Science, Information Science and Philosophy) is limited. Considering the scope of information ethical challenges contemporary society faces due to the fourth industrial revolution (4IR) and artificial intelligence (AI), it is apparent that discourses across disciplines, contexts, generations, culture and educational and societal levels must be cultured and facilitated with rigor. The concept “Third Space” is widely used in educational, linguistic, information literacy and more recently in information behavior and participatory design. It can be physical, virtual, cognitive, or conceptual spaces where individuals can negotiate, reflect, and form new knowledge and worldviews by merging diverse perspectives across disciplines to construct solutions to complex human problems (wicked issues).

This study investigated the impact of regional technical criticality and gener-icness on industrial innovation performance, as well as the moderating effect of collaboration depth and breadth. The findings provide a scientific founda-tion for government agencies to fine-tune their technology development strategies. The first four digits of the International Patent Classification(IPC) code were employed to represent specific technical units and co-occurrence network metrics were adopted to measure technology criticality and generic-ness. Utilizing panel data from the Chinese biopharmaceutical industry, we discovered an inverted U-shaped relationship between industrial innovation performance and technical criticality and the technical genericness signifi-cantly enhancing innovation outcomes. Furthermore, we observed that deep-er collaboration flattened the relationship between innovation performance and technical criticality, while broader collaboration had a negative moderat-ing effect on the relationship between innovation performance and technical genericness.

Heuristic evaluation is a quick and cost-effective method for assessing the usability of digital technologies. Introduced in 1994, Nielsen’s Heuristics is a well-known set of 10 usability criteria used to identify usability issues in web-based information systems that have been adapted and modified to different technologies and contexts. In this poster, we present an online usability self-assessment toolkit, based on a contextual translation of Nielsen's Heuristic guidelines. This adapted version of Nielsen’s Heuristics was created to help citizen and community science (CCS) practitioners collect and respond to usability issues encountered as they develop online resources for CCS projects. Preliminary analysis indicates a validation of an adapted set of Nielsen’s original ten heuristics for use by CCS practitioners plus the inclusion of an additional heuristic guideline related to supporting community engagement. The CCS heuristic toolkit supports empirical self-assessment of online resources by CCS practitioners and contributes to an understanding of inclusive practices in the domain of CCS. 

The COVID-19 pandemic presented older adults around the world with unprecedented challenges to their mental and physical health and quality of life. The increasing adoption of digital services is a promising solution to bridge the digital divide and promote inclusion. This study assesses the impact of such services on older adults during the COVID-19 pandemic, through a systematic literature review. It used the Web of Science (WOS) to search for papers from January 2020 to May 2023, and found 62 studies that met the inclusion criteria. During the pandemic,digital services for older adults encompassed access to life-related information, healthcare, online support interventions via social media, and digital literacy and skill enhancement programs. These services were grouped into four distinct impact dimensions: physical health, mental health, digital social participation, and quality of life. Finally, the study offers guidance on digital services for older adults, suggests future research directions and methodologies, and explains practical implications.

Contemporary Japanese public libraries have undergone notable evolutions in their spatial design. This research explores the distinct spatial attributes exhibited by Japanese public libraries since the dawn of the 21st century. By searching the Japanese Bibliographic database (CiNii Research) using key-words such as ‘library AND architecture’ and ‘library AND space,’ we found 2,985 pieces of literature focusing on publications after the year 2000. Sub-sequently, we closely read these, supplemented by an in-depth case study. We identified 116 relevant works and extracted key spatial elements during our literature analysis. Our case study focused on the Ishikawa Prefectural Library, epitomising the contemporary Japanese public library architectural paradigms. Next, we scrutinised the foundational design principles of this in-stitution and evaluated floor space allocation across diverse functionalities by sourcing data from the institution's architectural blueprint. Our literary exploration identified 16 salient spatial elements, especially ‘Expansive Spa-tial Designs’ and ‘Areas for Relaxation.’ The case study revealed that the Ishikawa Prefectural Library accentuates spaces that galvanise intellectual and cultural pursuits, cultivating a fresh cultural ambience underpinned by interpersonal exchanges. The floor space analysis denoted that ‘Meeting Spaces,’ constituting 5.56%, and ‘Learning Spaces,’ comprising 4.17%, re-flect the library's core design ethos. Thus, contemporary Japanese public li-braries prioritise user-centric spaces, which is manifested in the inclusion of diverse functional areas. Future investigations should expand the scope to include multiple libraries for a richer, more intricate understanding.

In the 21st century, the construction of public libraries increasingly involves collaboration with stakeholders. Particularly in recent times, as public libraries have shown signs of ageing, civic collaboration has become paramount in the establishment of new institutions. This study aimed to elucidate the requirements of citizens concerning the avenues and topics of civic engagement during the development of public libraries post-2020. Employing a two-fold research methodology involving document survey and case analysis, materials pertinent to civic collaboration were gathered for nine public libraries inaugurated or finalised after 2020. A meticulous examination of these documents illuminated the dynamics, duration, and modalities of civic collaboration during the construction of contemporary public libraries in Japan. For the case study, a qualitative content analysis of the Toyohashi City Central Library was undertaken. Utilising the qualitative analysis software MAXQDA2018, codes were ascribed to discussions and citizen opinions regarding the new public library, which were subsequently used to discern the prevalent themes. Consequently, ‘Space’ emerged as an area of increasing interest, given its preponderance in coded references, underscoring citizens’ diverse expectations for services and events.

As electronic data evidence, WeChat Chat records has played an important role in civil lending cases. From electronic records management perspective, based on relevant legal provisions, this paper analysed 120 civil lending cases with WeChat Chat records as evidence, studies and summarizes the status quo of authenticity determination of WeChat Chat records in the current judicial practice in China, identifies the difficulties of the authenticity determination of WeChat Chat records, and put forwards stategies for ensuring the authenticity of WeChat Chat records as legal evidence, so as to advance the authenticity guarantee work of WeChat Chat records.

Agenda setting has enormous impacts on information communication in crisis situations. However, little is known about the relationships of agendas set by the news media, public opinion, and the policies. Taking COVID-19 as the research scenario, this study employed topic modeling to examine the media agenda, the public agenda and the policy agenda and analyzed their relationships through correlation analysis. Primary findings suggested that the news media, public opinion and the policies had set different agenda priorities for COVID-19 related issues during the pandemic. Moreover, the media agenda shared similar issue attention with the policy agenda, but the public agenda was not well matched with the media agenda and the policy agenda during the pandemic. The findings of this study contributed to the literature on the agenda setting in the digital environment and provided practical implications for crisis information communication. Future research directions were also discussed.

This qualitative study examines students’ source selection practices before and after targeted instruction in critical information literacy (CIL), where the assigned course instructor presents the material in close collaboration with an academic librarian. The primary goal is to identify the impact of collaborative teaching strategies that emphasize the authority and expertise of the academic librarian. Study participants are upper-level baccalaureate students in an advanced writing course required for graduation, and this study explores the potential applicability of the findings to other similar high-impact general education courses. This is a two-phase study, the first of which is completed and the second is currently underway. This report highlights the preliminary findings from Phase I.

博物馆服务质量是国家文化发展程度的重要标尺之一，完善博物馆公共数字文化服务是满足人民日益增长的精神文化需求的重要途径之一。社会公众是其直接服务对象，精准挖掘用户信息需求对博物馆公共数字文化服务建设具有重要意义。本文利用爬虫技术以204个国家一级博物馆名称为检索词获取社会化问答平台“知乎”中的UGC数据。通过描述性分析、词频分析以及聚类分析，从数值、词表到语义对文本进行多层级挖掘，识别用户信息需求。博物馆公共数字文化服务用户信息需求呈现出8大主题内容，包括观览体验、社会事件、旅游攻略、入馆攻略、观览攻略、博物馆动态、文物信息和博物馆信息，归纳总结为讨论评价型、观览攻略型和科普解答型三种需求类型，针对性用户需求提出了对应策略，以期为博物馆公共数字文化服务建设提供启示与参考。

[目的]知识组合发现是从知识元素重组的角度挖掘未来科学技术发展、融合机会的方法。本研究同时从科研平台和社交媒体两个维度展开，结合基于特征和基于网络两种方法策略提出一种既融合了多重语义，又兼顾了低中高词频知识元素价值与重要性的知识组合发现方法。

[方法]方法的整体思路主要分为两个部分：（1）从科学和社会两个角度考虑，构建并计算了知识元素相关的语义特征作为后续模型的热量基础；（2）基于网络的策略构建热传导模型融合多重语义，挖掘知识元素和文献之间的潜在关联，赋予中低词频的知识元素更多的连边；进一步结合基于特征的策略，计算知识元素加权Jaccard系数实现知识组合发现。

[结果] 基于2018年—2022年的文献样本集共识别预测出2141对组合，其中后续得到文献验证的识别结果1372组，预测结果23组，共1395组（65.17%）。

[局限] 未对挖掘出的知识组合做进一步评价分析；其次，新知识元素的描述、识别或发现仍是待解决问题。

[结论]本研究提出的知识组合发现方法具有一定的有效性和可靠性，可以为未来的知识组合相关研究提供方法性参考，发现出的知识组合可以为学术创新及学科发展提供决策性建议

[目的/意义]调查面向数字人文的网络档案资源服务现状旨在明确实践进展和经验，深化数字人文研究利用导向下网络档案资源服务建设的实践策略。[方法/过程]采用文献调查及追踪的方法收集全球代表性实践案例，基于案例公开信息归纳当前面向数字人文的网络档案资源服务建设的关键要素。[结果/结论]研究发现实践的关键要素包括建构综合性服务体系的服务理念，多元协同参与的主体机制，厘清服务对象即研究人员的类型与需求，利用导向的资源服务内容。最后，结合我国实践现状，基于现有案例分析提出面向数字人文建设网络档案资源服务的启示性方向：一是以供应为整体定位指导资源建设与要素配置；二是形成研究者与存档机构主导的双核协同机制；三是充分识别网络档案资源服务对象并建立连接；四是数字人文利用导向下开展资源服务内容建设。

[目的/意义] 智慧数据是整合数据资源，激活数据价值的重要方法，是破解目前文化遗产领域数据资源庞大但未得到有效利用困境的重要手段，同时也是数智时代文化遗产活化利用的内在要求。[方法/过程] 首先，本文对文化遗产智慧数据资源共建要素进行分析，识别出文博机构、科研机构、数字文化企业和社会公众四个主要的参与主体。其次，结合文化遗产领域的数据资源、数据标准、支撑技术，明确了从数字化、数据化、平台化到场景化的文化遗产智慧数据建设流程。最后，对文化遗产智慧数据的应用场景和价值进行了阐释。[结果/结论] 本文构建多主体文化遗产智慧数据建设逻辑与路径，探索文化遗产智慧数据价值如何在资源建设中逐步凝练，并在智慧服务应用中如何实现，促进文化遗产资源的活化利用，实现文化遗产智慧数据提质增效。

Digitization of ancient texts is an essential approach to protect and utilize the ancient books, and an important way to inherit the excellent traditional culture. The construction of the high-definition image database of the Yongle Dadian is an important practice in the digital construction and utilization of the Yongle Dadian, which comprehensively demonstrates the style and version of the Yongle Dadian, and promotes the systematization of the knowledge, and the utilization of the intellectualization. Based on this, this study adopted the case study method, taking the “Yongle Dadian High-definition Image Database” project as an example, and summarized the practical work of the digital construction from two aspects: integrating resources and collaborative construction, and synthesized the main points of the digital utilization from five aspects: knowledge annotating, citation retrieving, 3D models interacting, ancient book circulation trajectory visualizing. In addition, some problems with the project were discovered, such as incomplete collection of the ancient books, imperfect quality, insufficient publicity channels and imperfect incentive mechanism. This study proposes relevant mitigation strategies and provides suggestions that aim to improve the High-definition Image Database of Yongle Dadian and promote the construction and utilization of the digitization of ancient texts.

政府开放数据是公共服务透明化以及数字赋能智慧化的重要支撑，为推动其价值增益，辨析政府开放数据价值实现要素之间的因果关系与时滞效应至关重要。有鉴于此，本研究基于扎根理论方法与社会技术系统理论归纳了政府开放数据价值实现变量，构建了政府开放数据价值实现系统动力学模型并开展仿真分析。结果表明，基于扎根理论所构建的系统动力学模型能够更加全面地反馈政府开放数据价值实现的规律，数据交易频次、数字化政策数量、反馈便捷程度变量均显著影响政府开放数据价值的实现。

Mental healthcare access continues to be a significant challenge in the United States, with marked disparities across socioeconomic divides. As technology and algorithms increasingly play pivotal roles in healthcare access, understanding unique symptom manifestations in socioeconomically disadvantaged communities is crucial. In this context, we embarked on a comprehensive analysis of mental health search patterns in Alabama counties, leveraging Google search data. Distinct trends emerged between socioeconomically advantaged and disadvantaged areas. The advantaged regions predominantly showed clinically specific searches, suggesting a higher degree of mental health literacy or more considerable access to healthcare professionals. Residents in disadvantaged areas primarily utilized generalized mental health symptom terms, such as anxiety and depression, hinting at a potential awareness or resource gap. A similar pattern was evident for somatic symptoms, with the disadvantaged showing a preference towards generalized and pain-related terms. This trend could signify disparities in access to specialized care or inappropriate clinical treatments, raising concerns like potential opioid misuse. Additionally, counties with higher population density had more mental health-related searches, while predominantly African-American counties showed fewer, suggesting potential cultural or linguistic barriers. Our findings emphasize the potential of refining search engines to cater to diverse user needs and the importance of tailored health campaigns for marginalized communities. However, potential ethical challenges, such as unintentional exacerbation of existing biases, must consistently be recognized. Future research will elevate this analysis to a national scope, considering the effects of significant global occurrences, such as the COVID-19 pandemic, on search behaviors.

Moretti’s “distant reading” is regarded as a pioneer and an exemplar of computational literary studies. “Distant reading” puts forward a strict boundary between hermeneutics and quantitative analysis. The relationship between the two and the nature of quantitative analysis in literary studies are key issues in the theory of computational literary studies. This paper analyzes the claims and issues behind Moretti’s dichotomy, and introduces Bourdieu’s field theory perspective, pointing out that the nature of quanti-tative analysis must be understood in relation to the larger disciplinary discursive practices in which it is embedded.

Across the world, grassroots community organisations contribute to local socioeconomic development and community empowerment. Most of these grassroots community organisations initially start as informal groups autonomously formed by like-minded members of the community or shaped due to the influence of external agencies like large non-government organisations in the development sector. Different factors, notably the vision of leaders, desire for sustaining long-term operations, and financial gains, often influence these entities to embrace the formal shape of an organisation. Owing to this transition from informality to formality, grassroots community organisations also have to change their approach to recordkeeping practices from reactive to proactive ones. Although the recordkeeping literature has a strong focus on organisational practices, the literature hardly looked at the specific case of grassroots community organisations. The literature did not investigate the influencing factors that reshape the recordkeeping practices of grassroots community organisations. In this regard, the push-pull-mooring theory adopted initially by scholars in migration studies has the potential to be applied to grassroots community organisations’ recordkeeping practices to explain the influencing factors. Considering this, we aim to explore the potential application of the push-pull-mooring theory in the context of the transitional recordkeeping practices of grassroots community organisations.

The Information Action Briefs (IAB) introduced in this paper use the format of a policy brief and serve as a resource and action guide to develop and create transformational actions using information to advance the United Nations Sustainable Development Goals (SDGs). The IAB is designed to inspire and mobilize library and information science (LIS) professionals (researchers, educators, scholars, students, practitioners, and policymakers) to act locally or globally, and personally or collectively in applying information-related work to promote community well-being and socioeconomic sustainability. LIS professionals are called to research, create, and use an IAB to advance the SDGs, by addressing the general aspects of a particular SDG as well as focusing on more specific topics at a granular level. LIS educators can use the IAB as a teaching tool, assigning the IAB for students to conduct research, propose solutions and write about sustainable development. In this manner, we hope readers will be similarly inspired as the authors to use the IABs in a variety of ways to conduct their research and practice, and document their work while integrating the SDGs towards greater relevance and impact of their efforts in information work environments and beyond.

This panel addresses the limited utilization of Geographic Information Systems (GIS), open datasets, and artificial intelligence technologies in public library services for social good, focusing on the Great Chicago Area of the United States. The panel aims to enhance the "Diversity, Equity, and Inclusion" of information services by exploring practical strategies and examining the library staff and iSchool curriculum requirements.

The panel goals include developing a new "GIS+ChatGPT" framework for improved information services and enhancing iSchool curricula. This initiative promises to provide accurate and relevant information for seniors and new immigrants, promoting community engagement and entrepreneurship. This panel builds on previous achievements in GIS applications for under-represented public library patrons.

Traditionally, scientific evaluation leaning on quantity and citation metrics rarely places a study within a specific context or a particular historical process for examination, making it difficult to fully reveal its substantial contributions. In a specific scientific field, each study focuses on a certain topic and corre-sponds to a certain evolutionary stage of the topic. However, few studies ana-lyze research contributions from context-oriented and process-oriented per-spectives. This study investigates the contributions of research under several representative topics in the field of quantitative science studies, using articles published in the international journal Scientometrics as samples. BERTopic model is employed for topic clustering, and four research topics are selected for in-depth analysis. In order to unveil research contributions to knowledge production and to different audiences, various metrics including disruptive-ness, citation impact and altmetrics are combined for indicator-level analysis, and articles are classified into different categories according to the knowledge contribution types and research orientations for content-level analysis. Results reveal that representative research topics exhibit greater disruptiveness and re-search impact compared to the overall sample. However, as research topics develop, there is a declining trend in introducing new knowledge and produc-ing impact within academia. Simultaneously, there is a certain degree of en-hancement in their impact beyond academia, and also a shift in knowledge contribution types and research orientations. Our findings contribute to a con-textual and processual understanding of diverse research contribution, serving as a reference for the evaluation practices of research outcomes oriented to-wards contribution assessment.

We present our approach for identifying the importance of citing sentences, where the importance of citing sentences is termed “micro citation importance” in our research. This approach characterizes a regression method based on the pretrained language model SciBERT, where the citation function is incorporated into the citing sentence as its input. Remarkably, our approach demonstrates superior performance on the 3C Citation Context Classification Shared Task corpus, suggesting that both regarding micro citation importance identification as a regression problem and integrating the citation function contribute to enhanced performance. Furthermore, we extend our investigation to literature evaluation, introducing a novel metric coined “micro citation frequency” derived from micro citation importance acquired by our proposed regression method. Notably, it is observed that micro citation frequency outperforms the established metric of citation frequency in terms of evaluating high-quality papers, further validating our proposed regression method. Our work not only enriches citation content analysis, but also holds implications for optimizing literature evaluation.

Interdisciplinary collaboration has emerged as a pivotal driver of academic progress and innovation. As researchers work across disciplinary bounda-ries, it becomes imperative to identify and examine the factors that affect the pace of scientific knowledge dissemination. This study introduces cita-tion lag as a metric to gauge the knowledge diffusion speed in Library and Information Science research. It further explores the factors associated with citation lag from both disciplinary and publication perspectives. Our re-sults indicate that an article is less likely to be cited if it remains uncited within 24 months after publication. Both the disciplinary attribute and the Open access mechanism were found to have a significant impact on citation lag. Furthermore, the result reveals a negative correlation between publica-tion lag and citation lag, implying that rigorous editorial review processes may ensure article quality and accelerate knowledge diffusion. Drawing on these insights, this paper endeavors to provide valuable guidance for craft-ing scholarly articles and facilitating knowledge diffusion.

Within the iSchools community, early career researchers (ECRs) play a significant role because they represent the prospective academic leaders who will shape the future of the information field. This panel is organized by the Asia-Pacific iSchools Next Generation Committee, which consists of early career scholars who aim to maximize the visibility and communication of ECRs. It focuses on the exchange of ideas and thoughts between leading senior scholars and ECRs within the iSchools around academic career support and development. We will conduct presentations and discussions about the role of research funding as an external catalyst and research methods as an internal driver in fostering the development of early career researchers (ECRs). This panel will benefit the iSchools community in planning future support for ECRs’ career development and ought also to increase the cohesion and commitment of the community.

Exploring how misinformation manipulates individuals can reveal the logic of information manipulation, and help better deal with the spread of different kinds of misinformation online including AIGC misinformation. To that end, this study conducted a reflexive thematic analysis based on 185 misinformation cases. The study found that there are 6 main manipulation mechanisms and 13 strategies behind misinformation. The 6 main mechanisms include providing clues to indicate credibility, reducing the psychological distance, expression of emotions to control the heuristic cognition, simplifying the difficulty of understanding, making content vivid to stimulate cognition and behavior manipulation. According to the result of reflexive thematic analysis, the misinformation manipulation model has been established. The results are helpful to fight misinformation online.

Public libraries are expanding their role with a growing emphasis on their potential to address a prominent contemporary social issue: overcoming social divisions. However, the perceptions of community members concerning libraries’ expanded role remain largely unknown. This study aimed to elucidate the experiences and perceptions of local community residents regarding public libraries’ role in mitigating social disparities. A case study was conducted at Deichman, a public library system in Oslo, using an online questionnaire focused on exploring the motivations behind library visits and identifying the library services deemed essential by residents. Based on 501 responses, the findings suggest that they predominantly associate libraries with their traditional roles of book lending and information provision, both in terms of their personal usage and the importance they attribute to various library services. In contrast, services aimed at bridging social divisions were not frequently deemed significant. Nevertheless, there were instances where respondents highlighted unique experiences facilitated by the library, including the connecting of diverse communities. These interactions are essential for reducing gaps in experiences and promoting mutual understanding among residents. It is anticipated that as more individuals encounter such transformative experiences, the overarching perception of public libraries will evolve accordingly.

Serendipity is broadly understood as a beneficial and unsought discovery triggered by chance and sagacity. The increase in using AI for product recommendations and promotions has changed underpinning conditions for serendipity and, thereby, people’s serendipitous experience. Today, online information encountering is artificially created to target individuals and they are not random nor by chance. We refer to such an encounter as ‘artificial serendipity’ (AS). Many consumers are now aware that algorithms may trigger encountering and are semi-expecting a beneficial discovery to happen when browsing the Internet. Some may even learn to reverse machine learning for their purposes, while others have unpleasant experiences of artificial serendipity. The existing serendipity models do not sufficiently address the design, activities, processes, and consequences relating to AS. The importance of AS requires revisiting, revising, and incorporating AS into the current serendipity models.

[目的/意义] 本研究通过分析健康焦虑人群的信息认知、信息认知偏差及其影响因素，探索信息认知偏差形成原因与过程，拟发现健康焦虑人群信息认知存在的问题，为网络健康信息管理提供参考。[方法/过程] 采取深度访谈法收集有关健康焦虑人群信息认知的访谈资料，进行扎根理论编码分析，识别健康焦虑人群的信息认知的主要环节、信息认知偏差、影响因素，进行专家咨询与调查问卷分析，对健康焦虑人群的信息认知偏差进行实证检验，分析与解读信息认知偏差、影响因素及它们之间的作用关系，探讨信息认知形成的过程与原因。[结果/结论] 健康焦虑人群信息认知所受的刺激是外部刺激和焦虑敏感性，其信息认知过程包括对信息的感知觉、注意、表征、记忆和推理5个主要环节，在刺激的心理强度、问题解决的思维、后效和知识储备4种主要因素的影响下，形成无法忍受不确定性、灾难性思维、证实性偏差和健康信息注意偏向4种典型的信息认知偏差。

[Purpose/Significance] This article explore the influencing factors of online health information adoption among digital immigrant, in order to improve the current status of health information adoption and utilization, bridge the digital divide, and enhance the digital dividend of health. [Method/Process] This research introduces information ecology theory and social emotional choice theory, using statistical analysis and qualitative comparative analysis to explore the motivations and configuration types of digital immigrant groups adopting online health information.[Result/Discussion] Quantitative analysis found that the emotional valence of information, relationship strength, and future time insight significantly affect the willingness to adopt health information. The moderating effect of future time insight exists, while the impact of technical anxiety is not significant; Qualitative analysis found that there are four types of configurations that generate high willingness to adopt health information, which can be classified into three categories: demand driven, emotional management, and passive acceptance.

摘要:[目的/意义] 从不同信息加工模式下研究在线健康社区用户风险感知行为，发现用户对风险感知的显著影响因素，对进一步帮助我们了解社区用户应对风险的机制，从而采取相应的措施提高用户在线健康社区使用参与率，促进公众健康知识的传播和国内在线健康社区的持续健康发展具有重要意义。[方法/过程] 以双重信息加工为理论基础，构建在不同信息加工模式下信息质量、情感支持、系统质量以及自我效能对风险感知的理论模型，并通过网络平台发放问卷，收集数据。[结果/结论]系统质量和自我效能对启发式和系统式信息加工均有显著的积极影响，而信息质量、情感支持以及用户的启发式的信息加工消极影响了用户信息隐私风险感知。在线健康社区的运营，要重视平台信息质量，增强平台专业化健康信息交流，促进用户的健康信息素养，营造良好的社区交互氛围。

[目的/意义]基于现行医疗数据行业标准中的数据分级结果模糊，且缺乏对分级要素定量测度的现状，通过挖掘医疗隐私文本，从客观认知视角进行医疗数据隐私计量，为验证与改进现行医疗数据分级结果提供参考。[方法/过程]医疗数据行业标准、法律法规、学术论文和泄露案例构成敏感数据识别来源，医疗敏感名词（数据项）、敏感动词和敏感程度词等敏感词汇组成的敏感数据单元构成敏感数据识别模型，敏感词汇的敏感性、语义强度和文本力度等指标构成医疗隐私计量模型。[结果/结论]计量结果表明，医疗应用数据（医疗检验数据、治疗过程数据、医疗记录数据）和健康状况数据（主诉与既往病史、现病史与生活方式、体格检查数据）的隐私性最强、医疗支付数据（医疗费用数据、支付方式数据、医疗保险数据）的隐私性其次、个人属性数据（个人身份数据、人口统计数据、个人联系方式数据）的隐私性最低。

[Purpose/Significance]There is no “common norm or value” of privacy between East and West. Under different cultural perspectives, there are differences in many aspects such as values, communication modes and interaction norms, which can cause cross-cultural differences in privacy. [Method/process]Privacy is placed in a specific realistic situation to explore what people are talking about when they talk about privacy. The content of Weibo and Twitter posts containing “COVID-19” and “privacy” from 2020 to 2022 is captured as a corpus, and the mixed method of structural topic modeling and manual interpretation is adopted for analysis and interpretation. [Result/Conclusion]Privacy is a multi-dimensional concept, which can be interpreted from different dimensions such as individual, technology, law, group, organization and practical practice. Due to the difference of collectivism and individualism between Chinese and Western cultural values, Chinese people are more able to appropriately surrender their personal privacy to public interests when facing the public interest needs of health emergencies. In the English Twitter corpus, personal privacy always occupies a prominent position, and the public pays more attention to privacy, and restricts the government's intervention and management of emergencies through the exercise of negative rights. There are many similarities in people's understanding of privacy in the technical dimension from different cultural perspectives, especially the concern about the risk of privacy disclosure caused by new technologies. The research results can enrich the academic community's understanding of privacy from the macro cultural background and social reality, and also provide a reference for the cross-culture comparative study of privacy.

Uyghur Muqam, a comprehensive musical composition originating from the Xinjiang Uyghur community, has been recognized as a world intangible cultural heritage. Presently, the focus of research has shifted towards the digitalization of Uyghur Muqam, an area of significant importance for the protection and development of this intangible cultural heritage. The establishment of a semantic web holds paramount significance in facilitating the digitalization process, particularly in enhancing the design of relational databases and dynamic digital libraries. This study aims to develop a semantic framework tailored to the unique attributes of Uyghur Muqam, thereby effectively capturing its essence.

This study explored the interdisciplinary influence of review articles published in library and information science (LIS) journals and classified the results by topic. In total, we identified 20,715 citations of 582 LIS review articles published between 2000 and 2019. Among the review articles, which were classified under 12 topics, those classified as “citation analysis” review articles received the most citations on average, despite covering the topic with the second-lowest number of review articles. “Information organization” review articles increased the most in annual influence and became the most influential category of review articles from 2017 onward. The review articles that explored information science–oriented topics tended to be more influential than those that explored library science-oriented topics. Regarding the influence of LIS research, approximately 35.2% of the citations of LIS review articles were in works published in LIS journals, indicating that the interdisciplinary influence of LIS review articles was greater than their intradisciplinary influence. Only the review articles that explored “LIS services,” “archives,” and “other aspects of LIS” had more influence on LIS research than on other research fields. The results of the present study can help LIS researchers to identify potential non-LIS collaborators who are interested in LIS research.

First and corresponding authors are often regarded as leading members in a research team. Distinguishing the gender composition of leading authors paints a nuanced picture on the gender differences in scientific performance at the research team level. This poster selects scientific publications in 2010 in the field of biology and finds that, in mixed-gender teams, while female authors hold a higher rank trend in publications’ bylines than male authors, female-as-corresponding-author research teams tend to receive less citations.

Understanding the patterns of information dissemination on social media platforms with different content distribution mechanisms becomes crucial to mitigate potential negative impacts and foster a healthier information ecosystem. However, existing studies still face the limitations of data availability, platform specificity, and neglecting concurrent patterns. To address these issues, we employ trending topics as an instrument to investigate information dissemination patterns across various platforms. A multivariate time series deep learning model combined with explainable artificial intelligence (XAI) techniques is utilized to capture the relationships and influences among internal popularity and external rivalry in the trending topic dynamics. For comparative study, we've meticulously constructed a large-scale dataset of over 8 million time-series trending topic data from Weibo (emphasis on the social network mechanism) and Douyin (prioritize the recommendation algorithm). We find that dynamic patterns of trending topics are significantly different under distinct platform mechanisms, particularly in the impact of external rivalry. This study introduces a new research perspective to explore information dissemination patterns across platforms with different mechanisms, and provides valuable insights into analysing the potential impact of platform mechanisms on trending topic dynamics.

To promote the high-quality development of public cultural services, libraries need innovative marketing methods to attract users’ extensive participation. Ritual activities can enhance group unity, promote cultural inheritance, and influence users’ identity, and can be used as one of the ways of library marketing. In-depth study of library ritual activities and clarification of the impact of ritual activities on library users’ identity is conducive to optimizing library ritual activities and enhancing library social value. Therefore, this paper interviewed 6 librarians and 15 users about their views on library ritual activities. Based on interview content and interactive ritual chain theory, social identity theory and communication ritual concept, this paper constructed a model of the influence of library ritual activities on user identity. Experiment was used to verify it. It was found that library ritual activities had a positive impact on user identification, however, perceived value and emotional energy did not play a mediating role in all effects, and there were certain differences between participating users and observing users.

Identifying reuse patterns of novel technologies is crucial for understanding technology diffusion and predicting technologies with a higher potential val-ue. Prior studies predominantly employed S-curves to depict technology re-use. However, the reuse of distinct technologies may demonstrate diverse patterns. Thus, this study attempted to uncover potential technology reuse patterns through a comprehensive cross-domain analysis of technology re-use. We conducted a shape-based clustering analysis on the reuse trajectories of novel technologies that emerged from 1995 to 2020 to identify potential technology reuse patterns. The findings indicate that the trajectories of 39,376 novel technologies exhibit four types of reuse patterns, i.e., s-shaped trajectory pattern, fleeting trajectory pattern, linear trajectory pattern, and ex-ponential trajectory pattern. The robustness of identified patterns was con-firmed through consistency analysis with the clustering results based on Eu-clidean distance. This work contributes to the comprehension of technology reuse and provides insights for forecasting the diffusion and application of novel technologies.

Health issues are prevalent among international students, and access to health information can help them manage their conditions and maintain their well-being. However, they are usually meet various barriers when they access health information. This study interviewed 11 international students in the United States to explore their behavior in seeking health information. The analysis revealed that their information-seeking patterns change once they arrive in the U.S. to attend university. They rely more on friends and the Internet in choosing information sources and use multiple languages for their search behavior. They also encounter more barriers in health infor-mation access. International students worry about language and cost as well as contend with a lack of understanding and distrust of the medical system, all of which hinder their access to information. These findings have implica-tions for developing strategies to enhance health information seeking among international students.

[目的/意义]明确突发公共事件下信息源选择多样性对知识建构的影响机理，有助于优化信息搜寻策略及信息加工方式，提升知识建构效率。[方法/过程]基于问题解决情境理论、启发式—系统式模型以及知识建构相关理论，构建并实证基于SEM方法的假设检验模型；同时，对信息源选择组合为前因变量进行模糊集质性分析，依次对不同信息源组合进行组态分析，探寻各信息源组合对个体知识建构产生的影响机理。[结果/结论]问题认知、约束认知、涉入认知、参考标准四个要素均对信息源选择多样性具有显著正向影响，系统式加工对知识建构具有显著正向影响，而启发式加工则不对知识建构产生影响，“网络信息源+传统媒体信息源+人际信息源”、“网络信息源+人际信息源”、“网络信息源”三种信息源组合能够对个体实现知识建构产生组态影响。

摘要：[目的/意义] 随着信息技术的不断发展和个人信息泄露事件的不断增加，公众对个人信息安全的担忧与日俱增，其个人信息安全感状况令人堪忧。本研究旨在确定个人信息安全感的概念内涵和构成维度，为信息安全教育、政策制定和技术发展提供更深入的理论基础。[方法/过程] 本研究以社会认知理论为支撑，采用了文献调研和内容分析的方法，逐步明确了个人信息安全感的概念内涵和构成维度，并通过德尔菲法验证了研究结果的有效性。[结果/结论] 个人信息安全感是指个体在个人信息方面的安全感状况，是一个系统、整体、动态发展的概念，是在个体、信息环境、行为意愿三个层面的持续交织与互动中形成的，包括个人信息安全评估中的自我安定感、个人信息风险应对中的自我胜任感、人际交往中的个人信息安全需求满足感、个人信息处理活动中的个人信息确定控制感、个人信息安全中的行为倾向共计五个构成维度。

[Bcakgroud] With the enrichment of network platform types, users are no longer limited to using single platform to obtain knowledge but tend to expand the channels of content acquisition, generating and sharing content among multiple platforms. Therefore, it is of great significance to focus cross-platform users to try to explore their knowledge exchange behavioral patterns and user classification to understand cross-platform knowledge exchange behaviors and reveal cross-platform behavioral systems.[Methods] In this paper, we take 120 users in Bilibili knowledge board as the research object, and on the basis of user alignment, we obtain their attributes, contents, and interaction data on Bilibili and Weibo, construct cross-platform user classification model, and utilize the K-means algorithm to realize cross-platform user classification and its behavioral analysis.[Results] Cross-platform users have three types: Cross-platform bilaterally heterogeneous users, Cross-platform bilaterally homogeneous users, and Cross-platform unilaterally active users. Cross-platform bilaterally heterogeneous users account for the largest proportion, they will present different contents on the platforms based on their perception of the platforms; Cross-platform bilaterally homogeneous users present similar contents on the two platforms, but there is a large difference in the degree of interaction between the two platforms; Cross-platform unilaterally active users account for the smallest proportion, they only active on one platform, the degree of input and interaction are significantly higher than the other platform. [Innovation] This paper constructs a cross-platform user classification model, reveals the cross-platform bilaterally develop strategy as the mainstream for cross-platform users, and raises suggestions for knowledge exchange services based on user classification.

【目的/意义】基于多用户属性与信任关系，探寻提高好友推荐效果的方法，提升用户体验以推动网络知识社区发展。【方法/过程】首先，采用文本挖掘、因子分析等方法对网络知识社区的多种用户属性进行分析；其次，计算用户的综合相似度和全局信任度，构建网络知识社区好友推荐模型。最后，以“科学网博客”为例，进行实证研究以评估推荐效果。【结果/结论】融合综合相似度与全局信任度的好友推荐模型比仅基于综合相似度与仅基于单一属性相似度的推荐模型具备更好的推荐效果。【创新/局限】从用户节点和网络全局两个角度进行多用户属性信息融合，能够有效提高好友推荐质量。局限在于未考虑到兴趣与互动行为随时间变化的动态因素，模型有待进一步完善。

摘 要：[目的/意义］在国家科技竞争与对抗的背景下，发掘与识别影响国家战略部署的科技前沿卡点，对于推动总体性国家安全发展与科技自立自强具有重要决策支撑作用。［方法/过程］首先整合知识图谱可视化知识基础，构建战略科技前沿风险事理构组图谱；其次通过四维点位测度指标体系确定科技前沿“卡脖子”点位备选数据集合；采用基于链路因果计算与深度学习模型的因果推理方法挖掘备选点位中的潜在卡点，并生成“卡脖子”卡点发生结构图；最后通过模型分类器输出卡点发生概率并对其进行路径概率对比，筛定最优科技前沿“卡脖子”卡点发现路径。［结果/结论］结果证明，该方法能够有效发现战略科技卡点并将其发生路径可视化，降低专家型情报决策方法的主观偏见性，提高了国家战略需求与情报决策的智能匹配率，能够对情报决策结果背后的原因进行有效追溯并推动智慧型情报模式的优化升级。

Within the iSchools community, early career researchers (ECRs) play a significant role because they represent the prospective academic leaders who will shape the future of the information field. This panel is organized by the Asia-Pacific iSchools Next Generation Committee, which consists of early career scholars who aim to maximize the visibility and communication of ECRs. It focuses on the exchange of ideas and thoughts between leading senior scholars and ECRs within the iSchools around academic career support and development. We will conduct presentations and discussions about the role of research funding as an external catalyst and research methods as an internal driver in fostering the development of early career researchers (ECRs). This panel will benefit the iSchools community in planning future support for ECRs’ career development and ought also to increase the cohesion and commitment of the community.

突发公共卫生事件成为了21世纪威胁人类健康安全的重大健康危机之一。在抗疫时期，网络成为了突发公共卫生事件时期群众了解信息、与人沟通的主要渠道。当不同的观点、想法、情绪在互联网中相互碰撞时就极易引起网络舆情风险。因此，在全球性的突发公共卫生事件频发的背景下，做好网络舆情风险的管理与控制具有重要意义。本文提出了一个突发公共卫生事件网络舆情风险分级方法。在指标体系的构建部分，利用等级全息建模（Hierarchical Holographic Modeling，HHM）框架识别突发公共卫生事件网络舆情风险因素并构建指标体系；在多准则决策方法部分结合层次分析法排序II（Analytical Hierarchy Process Sort II , AHPSort II）和摇摆赋权（Swing Weighting , SW）方法以及模糊理论，提出了一种新的模糊多准则决方法。该方法被应用于突发公共卫生事件时期微博平台的网络舆情风险研究。最后，根据研究结果，提出了一些有效的舆情风险管理建议。本文可以为政府在今后可能发生的突发公共卫生事件中制定或完善相关舆情风险治理政策提供参考。

为实现茶文化信息资源在语义层面的整合，识别其文化基因对其知识内涵进行细粒度挖掘和逻辑关联建构对于茶文化的现代化传承和发展具有重要意义。本文尝试将文化基因理论融入茶文化信息资源的知识重构中，通过将文化信息资源中的茶文化基因进行提取、识别，引入知识元组织方法进行知识关联形成知识资源，依靠知识图谱从多维度完成对茶文化信息资源知识服务实现路径的探讨。通过实证研究，验证了文化基因理论融入文化信息资源进行语义关联分析的适用性与可行性，知识资源的细粒度解构和重组方案可以为茶文化领域的多维重组与融合应用提供借鉴，也为中华优秀传统文化的系统性保护和文化基因的赓续发展提供参考。

科研合作预测一直是学界广泛关注的问题。科研合作网络同时具备社会网络和知识网络双重特征，使得准确预测合作关系成为挑战。现有研究大多通过探究文本内容和网络结构两大特征，利用链路预测方法预测潜在合作关系。早期大量研究关注基于相似性度量的合作预测，随着网络表示学习方法的兴起，学者开始关注基于表示学习的链路预测，利用文本表示学习和网络表示学习方法捕捉文本内容特征和网络结构特征。然而，如何有效融合科研网络结构和文本内容特征以提高科研合作预测性能仍是一个挑战。基于以上问题，本文提出了一种基于表示学习和图注意力网络的科研合作预测模型。具体来说模型包含以下四个阶段：第一，通过有偏的随机采样策略，构建基于学者合著关系的科研合作网络；第二，通过DeepWalk、BERT等网络表示学习和文本表示学习方法，抽取学者研究内容特征和网络结构特征；第三，采用注意力机制获取注意力系数，并将研究内容特征和网络结构特征输入到设计好的图注意力网络GAT中，利用邻居聚合策略融合两类特征以获得更新后的学者综合表示；第四，使用学者综合表示计算节点间的相似性，并基于随机森林进行科研合作预测。最后，本文通过多种基线实验和消融实验，在多个子数据集中证明了方法的有效性。

As an important center for the protection and research of global cultural heritage, the British Library holds a rich collection of global manuscripts and has undertaken numerous outstanding manuscript digitization and utilization projects. Taking the "Hebrew Manuscripts Digitization Project" as an example, this paper employs a case study methodology to interpret the distinctive features of manuscript digitization within the project, focusing on the development of a standardized manuscript digitization process and the use of a convenient metadata management application. The paper further uncovers the project's experiences in utilizing digitized manuscripts in four aspects: constructing a digital manuscript platform, developing 3D manuscript models, setting up virtual manuscript exhibitions, and establishing digital manuscript awards. Based on these findings, this paper recommends that Chinese ancient book preservation institutions should start from establishing specialized funding organizations, seeking international cooperation, standardizing digitalization standards for ancient books, innovating in display forms of ancient books, and setting public participation awards to digitize and utilize precious Chinese ancient books, thereby promoting the inheritance and enhancement of Chinese culture.

摘 要：[目的/意义]基于斯宾诺莎情感理论进行弹幕视频的方面级情感演化分析可以为视频制作者评价科普效果、提升视频内容质量提供自检方法，也能为舆情督导部门鉴别非理性弹幕提供一定参考。[方法/过程]以斯宾诺莎情感理论为基础，基于弹幕与视频内容的对应关系构建时间序列模型，以B站人工智能科普类视频为例，运用LDA主题聚类、情感分类、可视化等多种方法，讨论了各个主题的弹幕匹配程度并对每个主题进行方面级情感分析，根据该结果分别绘制了一般情感趋势曲线和主动情感趋势曲线，并进行对比分析。[结果/结论]研究结果表明，在方面级情感分析过程中，观众的弹幕内容从被动的消极情感逐渐走向主动的积极情感；引入斯宾诺莎情感理论的情感演化分析得到的演化趋势相较于传统的演化趋势更加清晰，且能够展示出传统分析中被忽略的影响因素。

Exploring the archives’ image in the eyes of Generation Z holds significant value for archives in shaping their future image management and fine-tuning public services. Employing the Destination Image Theory, this research utilized data from the Xiaohongshu platform to unveil how Generation Z perceives archives from both cognitive and emotional perspectives. In June 2023, researchers collected 1000 notes related to "archives" using Python, followed by content analysis utilizing Weiciyun platform and MAXQDA. The findings indicate that in terms of cognitive image, archives, as public spaces, encompass both functional and leisure aspects, with Generation Z showing particular interest in transportation location, architectural design, infrastructure, and archival resources as spatial components. In the realm of emotional image, Generation Z, while categorizing their emotional tendencies towards various facets of archives into "positive emotion-oriented" "neutral positive emotion-oriented" and "neutral emotion-oriented" overall demonstrates a positive emotional inclination. For Generation Z, archives is a niche but unexpected treasure space.

With the acceleration of the digitization process, Internet misbehavior has brought various negative effects on citizens' property, physical and mental health, and has become a major factor that harms social order and hinders national security. The practice of countries around the world shows that improving citizens' digital literacy is an effective way to solve these problems, and constantly enhancing the digital literacy and skills of the whole people and bridging the digital gap are also the realistic needs of the current situation in China. On the basis of social constructivism, this study explores the connotation framework of "citizen digital literacy" suitable for the Chinese context, constructs the internal and external dual-cycle structure of citizen digital literacy, and extracts 12 components of citizen digital literacy framework in the Chinese context by combing the relevant digital literacy framework and analyzing the needs of five contexts, including politics, economy and culture. It is explained in detail from three dimensions of knowledge, skill and attitude. Finally, based on the perspective of government attention, this paper analyzes the content of 139 digital literacy related policy texts, judges the current policy focus, and puts forward implementation suggestions from two aspects: balancing the policy focus and adjusting the policy focus.

文章立足于中华文化走出去的时代使命，探索中国图书馆学教育文化影响的叙事方法。采用文献研究法和历史研究法，在阐述国内外对文化影响的代表性理论研究成果基础上，结合教育与文化的关系和教育对文化影响的理论成果，构建起中国图书馆学教育文化影响的叙事框架。中国图书馆学教育对文化影响叙事方法主要包括图书馆学教育对文化的保存传承、选择传播、融合发展和创新创造4个维度。

[Purpose/Significance] The open government data platform is a carrier for online access and acquisition of government data, and how to improve the utilization level of the platform is an important issue facing the promotion of open government data. [Method/Process] Taking 16 provincial open government data platforms in China as the research object, combining the TOE framework, introducing the necessary condition analysis method, combining NCA and fsQCA, analyzing the two types of causality, necessary and sufficient, and exploring the key factors and effective paths that affect the utilization level of China's provincial open government data platforms. [Result/Conclusion] The study finds that there is no single necessary non-sufficient condition that affects the utilization level of provincial open government data platform, and that three antecedent variables, namely, data analysis technology capability, data construction level, and organizational readiness, are the key factors affecting the utilization level of open government data platform. Four conditional grouping patterns that generate high utilization levels of provincial open government data platforms are found using the grouping perspective and summarized as data technology and platform construction driven under government-led logic, technology and environment driven under demand response relying on financial resources, organization and environment driven under financial assistance relying on platform construction, and environment driven under government-led logic relying on data technology.

Exploring the knowledge flow of micro-level knowledge entities between different disciplines is conducive to reflecting knowledge contributions more comprehensively and objectively. In this paper, a discipline dominance index is proposed to identify the discipline ownership of knowledge entities at the micro level, analyze it from a global perspective, and then quantitatively measure the interdisciplinary knowledge flow. The results show that biology, clinical medicine, physics, computer science, and engineering occupy the core of the network and export more knowledge to other disciplines. In the fields of social science and humanities and arts, the knowledge contributions of psychology, sociology, and management are relatively high. The idea of measuring knowledge flow at the micro level proposed in this paper can enrich the measurement dimension of knowledge flow.

[目的/意义]旨在研究影响用户采纳冲突性健康信息的因素，有助于了解复杂信息环境下用户的信息决策、信息采纳过程，减少冲突性健康信息对用户认知的负面影响，帮助作出科学的采纳决策。[方法/过程]首先，通过文献扎根与编码析出影响用户冲突性健康信息采纳行为的因素；其次，运用DEMATEL方法分析影响因素与关键程度，从中心度与原因度两方面揭示关键影响因素；然后，通过ISM方法构建影响因素多级递阶结构模型，确定各层级因素之间的影响关系；最后，从用户、健康信息与社会支持等方面提出帮助用户科学应对冲突性健康信息的建议。[结果/结论]影响用户采纳冲突性健康信息的关键原因因素包括用户基本特征、经验积累与健康信息类型；关键结果因素包括用户心理情感、感知权衡与健康信源可信度；重要性因素包括用户心理情感、感知利弊、情感支持与健康信源可信度、信息支持。冲突性健康信息采纳行为是表层因素、中层因素与深层因素共同作用的结果。可以通过提升用户的信息辨别能力、改善健康信息质量、增强社会支持等方面帮助用户科学采纳冲突性健康信息。

While many occupations turned to remote work during the COVID-19 pandemic, domestic work by definition requires workers to enter other people’s households, and they often work in close proximity to their employers. With domestic workers proactively handling COVID- 19 risks as part of their already precarious jobs, there is a need for a conceptual understanding of risk management to aid this occupational group during a public health crisis. Our findings emerge from a preliminary qualitative study interviewing occupational groups who adopted risk work practices during the pandemic, providing insight into their risk perceptions and practices. In this paper, we focus on paid domestic workers recruited to investigate how they engaged in situated ‘risk calculations’ to assess different risks present at work. This paper invites an initial discussion on risk practices, communication, and policy to support domestic workers during crises.

The construction of a comprehensive portrait of data searchers represents a crucial yet underexplored topic within the field of data retrieval. To address this gap, this study focuses primarily on two key aspects. Firstly, based on the literature review, this study constructs a comprehensive user attribute classification framework that encompasses 52 user attributes derived from the dimensions of user background, expectancy, experience, and system evaluation and usage intention. Secondly, the present study proposes a deep learning-based multimodal data model for inferring these four-dimensional user attributes. The model posits that leveraging deep learning methods to analyze the features of online behaviors, eye movement, facial expressions, and verbal commentary extracted from multimodal data ena-bles accurate inference of the four-dimensional user attributes. A user ex-periment was conducted to collect data. The results from deep learning and ablation experiments provide strong support for the proposed model. The findings suggest that deep learning analysis of multimodal data facilitates the inference of the four-dimensional user attributes. Notably, the proposed model achieved high accuracy in inferring user expectancy and background. Search behavior features and eye movement features are pivotal in accurate-ly inferring the four-dimensional user attributes.

This study compared the use of social media by British and Chinese news media at the early stage of COVID-19. By analyzing the British Broadcasting Corporation (BBC) Twitter account and China Central Television (CCTV) Weibo account, this research explored the similarities and differences in the health communication strategies applied by two national news media in the UK and China. The content analysis results showed that news updates and official responses were the two most common topics in both BBC Twitter and CCTV Weibo at the beginning of the pandemic. However, CCTV focused more on reporting the statistics of the pandemic, while BBC emphasized the new developments of the pandemic. Besides, the BBC mainly used linked articles to present information, while CCTV used more diverse formats. This study contributes to the global health communication scholarship by considering cultural and organizational factors in the differences of health information dissemination.

Douyin and TikTok belong to the same company, ByteDance, and share similar platform infrastructures. However, they target different markets and operate in distinct platform ecosystems. Unlike copycat versions of Western platforms in mainland China (e.g., the Chinese Twitter—Weibo), Douyin and TikTok originate from the same source but flow into distinct user bases. Following the app walkthrough method and utilizing parallel platformization theory, this study explores the similarities and differences in presenting health information on the two platforms. This research contributes to validating and extending parallel platformization theory in the health domain.

In recent years, the “Young Adult Service,” which underpins the concept of “HOMAGO” proposed by Ito et al. (2009), has gained traction in public libraries, especially in Europe and the United States. In Japan, services that offer experiences with digital media are beginning to be introduced, drawing inspiration from Europe and the United States. Hence, this study seeks to clarify the usage patterns of teenagers at the Yokohama City Central Public Library. In this research, we conducted an observational survey focusing on the usage by teenagers on both the ground and basement floors of the Yokohama City Central Public Library, situated in one of the largest cities in Japan and which has recently undergone renovation. The study took place from the 22nd to the 24th of August and operated daily from 9:30 to 17:00. We examined variables such as the number of user groups, seating occupancy, resources utilised, and the nature of activities undertaken. Our findings indicate that Japanese teenagers predominantly use libraries for personal study but also engage in social chats using smartphones with their peers. These observations suggest that while Japanese teenagers primarily utilise the library for individual study, in group settings they engage in intimate in-teractions, often accompanied by reading or smartphone usage. This highlights a distinct “HOMAGO” (Hanging Out, Messing Around, Geeking Out) behaviour within libraries, emblematic of Japanese youth.

Archive management requires meticulous handling and precise stewardship of textual materials. Large Language Models (large language models (LLMs)), trained extensively on text data, possess exceptional text processing and interpretative capabilities. These allow for profound insights and extractions from the vast troves of information within archives. Anchored in the records life-cycle theory and archivists’ practical operations, this research explores the potential advantages of using LLMs in archival work. We begin by constructing a theoretical framework that demonstrates how LLMs can streamline tasks for archivists. Next, we introduce a novel LLM designed specifically for archive work, called Archival Generative Pre-trained Transformer (ArcGPT), and present its initial performance across four archival tasks. Recognizing that overarching performance metrics may not encapsulate the genuine user experience, we further propose a methodology for a user experiment designed to gauge the user-centric performance of how LLMs support archivists in their archival workflows.

The confluence of artificial intelligence (AI) and artistic creativity signals a pivotal shift in the arts, ushering in a new epoch where human-AI synergy demands further exploration. This research delves deep into the dynamic in-terplay of human and AI within the artistic domain. Through 20 detailed online interviews, we probe the experiential facets of information as humans and AI collaborate in creative endeavors. Our findings indicate that the role of AI in the arts transcends mere automation; it embodies a rich partnership where both human and machine continually learn, adapt, and innovate. This study also presents a model that outlines the information practices central to human-AI collaboration in creative activities. These insights highlight the transformative capabilities of AI, emphasizing the imperative for a more profound comprehension of this partnership and collab-oration. Such understanding is of referential significance, setting the stage for future research and advancements in the sphere of human-AI collaboration creative activities.

This 3-hour workshop will introduce the audience to an “Information Action Brief” (IAB) that is a tool (resource guide) to implement transformational actions using information to advance the United Nations’ Sustainable Development Goals (SDGs). It will assist members of the information community to develop their own IABs related to their research/teaching/service/engagement areas of interest during small breakout group sessions. Participating individuals develop ten action items for one of the 17 SDGs connecting to their work from an information perspective. Session results will inspire members to act locally or globally, personally or collectively to integrate the SDGs in the context of participants’ information-related scholarship. During the interactive workshop, real-world actions will be discussed to develop a comprehensive picture of the role of information in shaping progressive change in the world. Implications will involve transforming the professions in an emerging information advocacy role for the remaining decade to achieve the SDGs.

[目的/意义]古农书是不可再生的文化资源，记载了中华传统农耕文化，对其史料资源进行知识组织与知识发现可以推进古农书馆藏文献的数字化开发进程，有助于实现对古农书史料资源的深度开发与智慧服务。[方法/过程]本研究以“古代四大农书”为研究对象，构建了基于知识图谱的古农书资源语义知识组织框架。首先，通过对数据进行分析与整理，构建古农书史料资源本体模型实现对资源中的概念与关系的规范化描述，其次，基于已构建的模型进行知识抽取，利用知识融合技术来消除共指问题，最后将RDF数据存储到Neo4j图数据库中，实现对古农书史料资源的语义知识组织与多维知识发现。[结果/结论]本研究为古农书史料资源的语义知识组织与多维知识发现提供了研究方案，也为数字人文背景下古农书资源深层次开发与利用提供了新的研究视角。

中国传统戏剧类非遗是我国表演艺术的瑰宝，是中华民族智慧的结晶，也是广大人民群众喜闻乐见的艺术表现形式，承载着丰富的历史、艺术和社会内涵。随着数字技术的不断进步，现代技术为非物质文化遗产的数字化、知识化、智慧化转变提供了新的机遇，细粒度的语义描述和关联组织成为领域研究所需。本研究旨在借助ChatGPT+Prompt模式构建中国传统戏剧类非物质文化遗产知识图谱，从而促进对这一珍贵文化遗产的更深入理解和传承。首先，以中国传统戏剧类非遗为研究对象，通过爬虫技术获取原始文本数据集，设计了CRISPE架构的问答提示语（Prompt）来引导知识图谱构建的全过程。其次，基于大语言模型的ChatGPT，采用对话式的few-shot学习方式，设计了一套针对戏剧类非遗的知识本体，实现了知识的有效迁移。其次，通过ChatGPT进行知识抽取，成功识别出非物质文化遗产的实体和关系，并将其存储至图数据库Neo4j中，实现了Text2SOL的过程。结果表明，通过本文的研究不仅有助于数字化保护和传承中国传统戏剧类非物质文化遗产，还为构建领域知识图谱的新方法提供了有益的启示。通过结合大语言模型和Prompt技术，为非物质文化遗产的数字化管理和推广开辟了新的途径，提升了其社会和文化价值，同时也有望为其他领域引入大语言模型提供了新的思路。

[Purpose/significance] By focusing on agricultural texts to analyse the evolution of ancient scientific knowledge, we can discover the development of ancient science and technology and pass on the excellent traditional science and technology. [Method/process] The article first constructs a corpus of agricultural book texts based on sericulture book records, extracts keywords from the texts using the tf-idf algorithm, builds a co-occurrence matrix using the co-occurrence relationship of keywords, and then constructs a knowledge network of agricultural books; adopts the Louvain community detection algorithm for community division and topic discovery of the knowledge network; determines the knowledge community by calculating the community similarity of adjacent time evolution and visualize the presentation. [Results/conclusion] The research has discovered that the knowledge of mulberry cultivation and silk reeling was inherited stably during the Yuan, Ming, and Qing dynasties, which has enhanced our understanding of the knowledge system of sericulture history. It has also revealed other significant themes in sericulture history, particularly the importance of early silkworms in the field of sericulture technology and the consistent transmission of silkworm breeding themes within the sericulture knowledge cluster. Additionally, the research has uncovered the developmental features of historical sericulture technology knowledge, with a continuous accumulation of sericulture knowledge, reaching its peak during the Qing dynasty.

摘要：[目的/意义]提出融合三元空间大数据的重大突发事件的事件图谱模型，旨在支撑重大突发事件的全要素、全过程、全方位知识表示。[方法/过程]首先，从世界图景的概念出发界定事件图景的内涵和类型，并构建通用事件图谱模型，用以关联不同抽象层次的知识，承载多维立体的事件图景；其次，探究重大突发事件在物理-社会-网络三元空间演化过程，借助事物运动与信息传播两个过程，揭示重大突发事件的跨空间演化机理，构建重大突发事件的跨空间演化模型；最后，基于重大突发事件的跨空间演化模型构建重大突发事件的事件图谱模型。[结果/结论] 知识表示方面，重大突发事件的事件图谱不仅可以描述事件在三元空间的演化过程，揭示重大突发事件的演化模式，还可以呈现物理、社会和网络空间要素的状态及其间相互作用，揭示事件演化的内在动力。知识组织方面，重大突发事件的事件图谱能够融合三元空间的关键信息，关联碎片化知识，呈现一个立体、多维的事件图景。

Personalized recommendations have been widely employed in the commercial development of various content platforms, and users' awareness of them has deepened. While there is already a substantial body of research on the evaluation of recommendation algorithms from a user-centric perspective, there is a lack of research evaluating personalized recommendation algorithms from an ethical dimension. Therefore, this study, from the perspectives of both users and public interest, applies the normative analysis method to propose a new concept, 'Rationality of Personalized Recommendations on Content Platforms,' along with its three dimensions: Technical Rationality, Content Rationality, and Ethical Rationality. Building upon existing literature, this study formulates operational indicators and constructs a scale with the aim of confirming a theoretical model for constructing the impact of the rationality of recommendations on users' intention to continue use. An empirical research method is employed to explore the causal mechanisms. The results indicate that the newly developed scale for the rationality of personalized recommendations on content platforms exhibits high reliability and validity. Furthermore, it demonstrates a significant positive impact on users' intention to continue use.

Threats to the credibility of published research are real and serious (e.g., retractions, irreproducible findings, etc.). To enhance the reliability and value of published health research literature, the EQUATOR Network, begun in 2006, has expanded into a global initiative to promote transparent and accurate reporting of biomedical and health research studies through the development and dissemination of reporting guidelines for different types of research. Currently, thousands of journals endorse major reporting guidelines. However, challenges face researchers in using reporting guidelines correctly. This highly interactive workshop aims to build a new research community to develop effective solutions to these challenges: how to measure the effectiveness of enhancing reporting guidelines to improve the quality of publications, how AI can play a role in enhancing reporting guideline adherence, and how best to train researchers in selecting and implementing reporting guidelines. We developed a survey for readers and participants at this link https://utk.co1.qualtrics.com/jfe/form/SV_1AXWP4V9hMCDIA6

Maintaining a healthy lifestyle is essential for cancer patients, and certain lifestyle factors can help manage specific types of cancer. However, research has shown that lifestyle information is not effectively communicated during clinical visits, and patients may not receive appropriate guidance to manage their lifestyles during cancer treatment. This paper will develop an automatic compliance analysis method and examine the present state of lifestyle information documented in clinical notes, discover for which type of cancer patients are receiving the most appropriate lifestyle recommendations during clinical visits, and assess the extent of compliance between the lifestyle recommendations and published standard guidelines. Results indicate that the average frequency of selected lifestyle keywords appeared 4 times during each clinical visit, and only 3% of medical notes pertained to patients' lifestyle. Diet and weight-related keywords were the most frequently mentioned lifestyle factors, but the compliance was low for major types of cancer. The proposed method could have significant implications for improving cancer patients' lifestyle management by facilitating timely and accurate assessment of lifestyle recommendations from clinicians.

The national reading campaign in China is a strategically important initiative aiming at promoting citizens’ reading literacy. It has been recognized that the need to specifically target at rural areas, which shows lower reading levels compared to the national average. Family involvement is one of the crucial elements in driving reading promotion efforts. This study utilizes data from the Individual Confirmation database and the Child Proxy database of China Family Panel Studies (CFPS), and combines theories from sociology and pedagogy field related to family education and inter-generational transmission. This study employs descriptive statistics and structural equation modeling to examine the current state of women reading status in rural areas, as well as incorporates various factors such as educational perceptions, educational participation, offspring personality, and offspring educational performance to validate models of inter-generational influence on reading habits. The findings indicate that women in rural areas with child-raising responsibilities exhibit lower overall reading rates and volumes compared to the general population, although their reading behavior surpasses the regional average in the country. Notably, the study reveals that the reading behavior of these women significantly influences their offspring’s reading behavior and academic achievements through four dimensions: parenting perceptions, educational participation, family relationships and family expectations.