When users seek help on online health platforms, their expressions are diverse, unstructured, and cognitively complex, posing significant challenges for fine-grained users' emotion understanding. Existing approaches typically rely on commonsense associations to statically model relationships between emotions and events, overlooking dynamic cognitive processes underlying these connections. To address this shortcoming, we proposed the Cognitive-Affective Chain framework, grounded in Social Support Theory, the Theory of Mind, and the James-Lange Theory of Emotion, to analyze users' expression from a cognitive perspective. Based on this, we defined a novel task, Cognitive-aware Contextual Emotion Understanding (CCEU), which adapts Aspect-Based Sentiment Analysis to better capture the multidimensional and cognition-driven emotional content. To ensure fair and meaningful evaluation of large language models (LLMs) in cognitively demanding tasks, we introduced the Hybrid Generation and Classification Score (HGCS), a metric combining generation quality and classification reliability. Experimental results showed that LLMs can outperform baselines on HGCS by 15.56 %, even when F1 score drops by 2.12 %, demonstrating that HGCS can better reflect the capabilities of generative models in complex emotion understanding. Next, inspired by Dual Process Theory, we designed prompting strategies that simulate human-like reasoning, improving LLMs' performance in CCEU task. However, behavioral analysis revealed a bias toward information support over emotional support, exposing the gap between machine inference and human empathy. Taking depression as an example, this study established a cognitively grounded paradigm for emotion modeling in mental health support, also contributing to the development of fair, socially responsive, and cognitively aligned AI systems.

Accurately measuring policy diffusion intensity is crucial for understanding innovation dissemination mechanisms, yet existing approaches face limitations in capturing its dynamic nature. By applying text-driven methods to government documents, this research constructs a twodimensional quantitative indicator that integrates both hierarchical effectiveness and textual intensity to capture policy diffusion dynamics. Using the panel data analysis of 9091 government documents from 2007 to 2022, we systematically examine the diffusion mechanisms of China's low-carbon policies. Our findings reveal that learning, imitation, and coercion are the primary mechanisms driving the intensity of low-carbon policy diffusion, while economic competition plays an insignificant role. Furthermore, urban carbon emission levels and public environmental awareness promote policy diffusion, whereas energy consumption dependency inhibits it. By demonstrating an effective application of text mining techniques in measuring policy diffusion intensity, this study provides new methodological and empirical insights for understanding policy diffusion within multi-level governance systems.

The work‐task‐based framework offers a cohesive perspective for understanding workplace information behavior, guiding empirical exploration of information engagement in modern work environments. This study investigates both task descriptions and task processes of information‐intensive work tasks through diaries and follow‐up interviews to capture authentic user experiences. Data from 52 work tasks across diverse organizations reveal that the most frequent topics include Reference , Business , Science , Society , and Computers , with Intellectual and Decision/Solution product types being predominant. Performers typically begin with moderate or high work task knowledge. On average, each work task involves 2.5 seeking tasks and 5.3 search tasks. Seeking tasks are mainly linked to resolution‐oriented information use, while search tasks rely on external sources for factual resolution and verification. Work task topics, product, prior knowledge, subtasks, and duration significantly influence source selection and information use. As work tasks progress, the number of search tasks and clarification use decreases, whereas resolution and verification use increase. These findings refine theoretical models of task‐driven information behavior and provide practical insights for designing adaptive information systems and AI tools to better support evolving work task processes and enhance work performance.

This study examines the historical evolution of interdisciplinary research (IDR) over a 40-year period, focusing on its dynamic trends, phases, and key turning points. We apply time series analysis to identify critical years for interdisciplinary citations (CYICs) and categorizes IDR into three distinct phases based on these trends: Period I (1981-2002), marked by sporadic and limited interdisciplinary activity; Period II (2003-2016), characterized by the emergence of large-scale IDR led primarily by Medicine, with significant breakthroughs in cloning and medical technology; and Period III (2017-present), where IDR became a widely adopted research paradigm. Our findings indicate that IDR has been predominantly concentrated within the Natural Sciences, with Medicine consistently at the forefront, and highlights increasing contributions from Engineering and Environmental disciplines as a new trend. These insights enhance the understanding of the evolution of IDR, its driving factors, and the shifts in the focus of interdisciplinary collaborations.

This study examines the historical evolution of interdisciplinary research (IDR) over a 40-year period, focusing on its dynamic trends, phases, and key turning points. We apply time series analysis to identify critical years for interdisciplinary citations (CYICs) and categorizes IDR into three distinct phases based on these trends: Period I (1981-2002), marked by sporadic and limited interdisciplinary activity; Period II(2003-2016), characterized by the emergence of large-scale IDR led primarily by Medicine, with significant breakthroughs in cloning and medical technology; and Period III (2017-2020), where IDR became a widely adopted research paradigm. Our findings indicate that IDR has been predominantly concentrated within the Natural Sciences, with Medicine consistently at the forefront, and highlights increasing contributions from Engineering and Environmental disciplines as a new trend. These insights enhance the understanding of the evolution of IDR, its driving factors, and the shifts in the focus of interdisciplinary.

Scientific collaboration has become increasingly popular due to the growing complexity of scientific tasks, especially for scientific projects supported by large funding agencies such as The National Natural Science Foundation of China (NSFC). This study focuses on modeling the network incremental elements within the scientific collaboration process of NSFC project teams to understand the intricate knowledge growth mechanisms. Four elements representing incremental knowledge were defined: Isolation, Mixed Addition, Inclusion, and Internal Correlation. Additionally, four knowledge incremental patterns and different collaboration processes were identified. The study discovered the following key findings: (1) NSFC project teams prioritize knowledge absorption and integration during collaboration, predominantly advancing knowledge through Mixed Addition approaches. (2) Teams in Management Science and Engineering (MSE) discipline tend to expand through Mixed Addition approaches, while Economic Science (ES) teams prefer Inclusion and Internal Correlation approaches for team development compared to MSE teams. (3) The knowledge pioneering pattern negatively impacts productivity, while the emergence of knowledge expansion and enhancement patterns can lead to significant improvements. Overall, this study explores the team collaboration process from the knowledge growth perspective, which provides valuable insights for optimizing team management and improving collaboration efficiency.

While low-carbon development has become a common goal for cities worldwide, the mechanisms driving regional low-carbon policy diffusion remain inadequately understood. This study constructs a novel analytical framework by integrating competition, learning, and coercion mechanisms from policy diffusion theory. The framework conceptualizes cities as agents embedded in heterogeneous network structures, revealing how different network structures and diffusion mechanisms influence the spread of low-carbon policies through multiscale diffusion and dynamic game processes. Our findings show that low-carbon policy diffusion is jointly shaped by vertical and horizontal mechanisms, where cities respond to top-down institutional pressures while learning from peers. Network structural features, such as heterogeneous connections and key node cities, significantly affect the speed and scope of policy diffusion. This study extends the theoretical perspectives of traditional policy diffusion research and advances coordinated regional low-carbon actions from a network perspective.

This poster presents a systematic review of 98 user studies in Generative Interactive Information Retrieval (GenIIR), exploring how users engage with GenAI in interactive information‐seeking contexts. Guided by the Information Seeking in Context (ISIC) framework, we examined two key dimensions: Research Focus and Information Context . Our results show that most studies examined user perceptions and information behavior, while many also addressed system design evaluation. These studies covered diverse domains and user groups. The most common settings were general, health, and education domains. Participants were primarily general users or students, and these studies often involved public tools like ChatGPT. Other studies focused on professional domains and custom GenAI systems, where interaction context, user roles, and task environments were more specialized, highlighting ISIC's emphasis on context‐sensitive information behavior. These patterns reflect the accessibility of public tools and a rising emphasis on context‐sensitive system design. This review provides insights into developing context‐aware, human‐centered GenAI systems.

To achieve the ambitious carbon neutrality goal by 2060, the Chinese government has implemented a series of carbon neutrality policies. These policy documents are pivotal in facilitating the examination of policy substance, the scrutiny of policy evolution, and the dissection of the policy instruments involved. This study develops an analytical framework for assessing carbon neutrality through policy documents, applying text mining and network analysis to probe the intricacies of policy topics, interagency collaboration, and diffusion dynamics. This research aims to delineate and expound upon the strategic paradigms employed by the Chinese government in its quest for carbon neutrality. The findings reveal a constellation of eleven policy topics, with "green" and "low carbon" being key aspects of each. The policy collaboration network has a density of 0.593, and the National Development and Reform Commission (NDRC)'s high average weighted degree of 14.6 highlights its crucial role in leading and coordinating these policies. In terms of diffusion dynamics, the green energy transition topic has a diffusion speed of 0.967 and a strength of 49, indicating its importance to the Chinese government. On a practical level, the findings offer policy-makers concrete, actionable recommendations to refine policy design and enhance implementation effectiveness. Theoretically, this study advances the scientific understanding of policy dynamics by proposing a novel analytical framework that integrates multiple dimensions of policy analysis, contributing to the methodological development of policy research.

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

PurposeThe purpose of this study is to explore the evolutionary path and stable strategy for the competitive dissemination between disinformation and knowledge on social media to provide effective solutions to curb the dissemination of disinformation and promote the spread of knowledge.Design/methodology/approachBased on the social capital (SC) theory, the benefit matrix is constructed and an evolutional game model is established in this paper. Through model solving and Matrix Laboratory (MATLAB) simulation, the factors that influence disinformation-believing users (DUs) and knowledge-believing users (KUs) to choose different strategies are analyzed.FindingsThe initial dissemination willingness, the disinformation infection probability, the knowledge infection probability and the knowledge penetration probability are proved to be crucial factors influencing the game equilibrium in the competitive dissemination process of disinformation and knowledge. Moreover, some countermeasures and recommendations for the governance of disinformation are proposed.Originality/valueCurrently most research interest lies in the disinformation dissemination model but ignores the interaction between disinformation and knowledge in the diffusion process. This study reveals the dynamic mechanism of social media users disseminating disinformation and knowledge and is expected to promote the formation of cleaner cyberspace.

Revealing interdisciplinary patterns is a cornerstone for the continued evolution of research, education, and societal progress, providing a scaffold upon which to build a more collaborative and integrated approach to knowledge creation. This study presents a novel approach to identifying and analyzing the critical year for interdisciplinary citations (CYIC), which was defined as the year in which qualitative change in interdisciplinary knowledge flow occurred. We conducted two experiments using a Chinese paper dataset spanning 106 disciplines from 1992 to 2022, with the first to pinpoint the occurrence of CYICs and the second to examine three patterns of interdisciplinarity following these CYICs. Our findings revealed that 85% of disciplines exhibit CYICs, often corresponding with a transition from unidirectional output to reciprocal knowledge cooperation. Furthermore, we found that datasets after CYICs are generally characterized by increased interdisciplinarity of knowledge, albeit without a corresponding rise in the interdisciplinarity of disciplines or interdisciplinary diversity. Our results suggest that policy shifts and societal needs are pivotal in driving the formation of interdisciplinary collaborations, as exemplified by the surge in mutual interdisciplinary citations in response to China's poverty alleviation efforts and western development policies.

Revealing interdisciplinary patterns is a cornerstone for the continued evolution of research, education, and societal progress, providing a scaffold upon which to build a more collaborative and integrated approach to knowledge creation. This study presents a novel approach to identifying and analyzing the critical year for interdisciplinary citations (CYIC), which was defined as the year in which qualitative change in interdisciplinary knowledge flow occurred. We conducted two experiments using a Chinese paper dataset spanning 106 disciplines from 1992 to 2022, with the first to pinpoint the occurrence of CYICs and the second to examine three patterns of interdisciplinarity following these CYICs. Our findings revealed that 85% of disciplines exhibit CYICs, often corresponding with a transition from unidirectional output to reciprocal knowledge cooperation. Furthermore, we found that datasets after CYICs are generally characterized by increased interdisciplinarity of knowledge, albeit without a corresponding rise in the interdisciplinarity of disciplines or interdisciplinary diversity. Our results suggest that policy shifts and societal needs are pivotal in driving the formation of interdisciplinary collaborations, as exemplified by the surge in mutual interdisciplinary citations in response to China's poverty alleviation efforts and western development policies.

Cyberchondria, involving the excessive and compulsive seeking of health information, has garnered escalating attention in recent years. One model proposes cyberchondria as an addictive behavior, and craving is considered a key driver of addictive behaviors. However, craving has yet to be systematically studied in the context of cyberchondria. Therefore, this study aimed to conceptualize a contruct of health information craving, develop, and validate a health information craving scale, intended for subsequent empirical examinations in the context of cyberchondria. We employed a rigorous multi-step procedure for scale development and validation, conducting three separate studies with 1633 participants. The resulting scale of health information craving exhibited a multi-dimensional structure with positive and negative reinforcing sub-scales, as confirmed through diverse reliability and validity assessments. As expected, the scale demonstrated a positive relationship with individuals' cyberchondria, thus providing support for the nomological validity of the developed scale. Furthermore, the findings suggest that health information craving should be considered a related but distinct concept from another similar concept, health information need, thereby offering further support for its operationalization. Theoretical and practical implications are discussed.

The finance-level Artificial Intelligence of Things (AIoT) is going to become a novel media in the 6G-driven digital society. Inside the financial AIoT environment, large-scale crowd credit assessment with the guarantee of low latency has been a general demand. Facing limited computational resources, there is still a lack of effective computation offloading methods for this purpose to ensure low latency. In order to deal with such an issue, this article introduces edge computing mode and proposes a low-latency edge computation offloading scheme for trust evaluation in financial AIoT. With different elements involved in the assessment process being denoted via mathematical description, a multiobjective optimization problem with constraints is formulated. Then, the aforementioned optimization problem is solved by a specific search algorithm, so that optimal task offloading schemes can be found. To assess the performance of the proposal, some simulation experiments are conducted to verify the proposed task offloading method. And it can be reflected from numerical results that latency can be well reduced compared with baseline methods.

Cyberchondria during the COVID-19 pandemic has posed significant challenges to public well-being. Previous lstudies have implicated perceived health risk in COVID-19-related cyberchondria. However, the effect of perceived COVID-19 health risk on cyberchondria and potential underlying mechanisms remains unclear. In this study, we developed a model based on the general addictive behavior framework of the I-PACE (Interaction-Person-Affect-Cognition-Execution) model to describe how induction of health risk of COVID-19 resurgence may influence individual tendencies regarding COVID-19-related cyberchondria. Through a scenario-based experiment, we tested this model with a balanced sample of 984 participants from China. The results revealed that the effect of induction of health risk of COVID-19 resurgence on tendencies regarding COVID-19-related cyberchondria was fully mediated by individual's risk perception and health information craving. Additionally, moderation analysis showed that an individual's general inhibitory control did not moderate the development of cyberchondria. Theoretical and practical implications are provided.

Purpose This paper aims to conduct a comparative analysis of the scientific performance of distinguished young scholars in China during the pre-award and early stages of their research careers, aiming to provide insights into their growth pattern. Design/methodology/approach Spearman correlation was used to analyse the correlation between various academic ages and awarding age of the distinguished young scholars. The Wilcoxon matched pairs test was used to analyse variations in their scientific performance across different research stages. Findings The findings showed that: a) early successful research experiences significantly impact their emergence as outstanding scientists. While a low correlation exists between publication ages and awards, perseverance proves crucial for later-stage academic achievements; b) productivity increases before awards, with notable variations between first-author and non-first-authored publications; c) collaboration intensifies before awards, particularly in non-first author roles. However, discipline-specific variations highlight the importance of smaller teams and first-author roles, especially in the early career stage; d) the correlation between collaboration and productivity depends on research roles, emphasizing the evolving nature of collaboration dynamics as scholars progress in their careers. Originality/value This study could offer a reference for formulating well-founded talent training programs and reward mechanisms.

Identifying the path to realize the value of data elements is of great significance to accelerate the deep integration of digital economy and real economy. In order to explore the mechanism and path of realizing the value of data elements, this study combs the existing ways and attributes of data elements, clarifies the mechanism of realizing the value of data elements on this basis, and constructs the value realization path including basic activities, auxiliary activities and value development based on the value chain theory. The research shows that the value realization mechanism of data elements includes subject participation mechanism, marketization realization mechanism, ownership definition and transfer mechanism. Data collection, data organization, data circulation and data utilization are the basic paths to realize the value of data elements, while the three auxiliary activities of data security protection, data technical support and data talent guarantee ensure the realization of value.

The knowledge system of LIS ＆ Archives Management have undergone a long period of development, and its first-level discipline has been renamed "information resource management". As a first-level discipline name, information resource management highlights the characteristics of the times, covers the entire process of the field of library, information and archives, and opens up space for new fields. It is a concept that keeps pace with the times and integrates domestic and foreign information practice. After the renaming of the first-level discipline, an important issue that the academic community needs to solve is how to strengthen the construction of new majors. I suggest that both postgraduate and undergraduate levels can be considered comprehensively. At the postgraduate level, majors can be set according to the original secondary disciplines, or the connotation of information resource management, or a combination of the first two methods, which can not only retain the original secondary disciplines, but also contain new majors. At the undergraduate level, the major name of library and archives can be renamed as information resource management or information management to promote the connection between the undergraduate catalog and the postgraduate catalog.

[目的/意义]对《中国情报学历史与发展进程》一书进行评介,旨在使读者了解中国情报学的演变历程、发展阶段和发展规律.[方法/过程]运用内容分析、归纳演绎方法,从写作背景、整体内容、主要亮点等评析该书的主要特色.[结果/结论]该书基于系统丰富的史实资料进行研究,提炼了中国情报学发展过程中的精华,采用了新思路描绘情报学发展演进的历程,作为我国首部以中国情报学历史为研究对象的学术著作,弥补了中国情报学缺少历史研究著作的一项空白,将为"十四五"期间中国情报学的理论、方法、技术和实践的快速发展创造一定的条件,为今后的情报学研究者提供研究素材和分析基础.