[Purpose/significance] In the current era where algorithmic recommendations are deeply embedded in social operations, traditional metaphors represented by the "information cocoon" are insufficient to fully explain the complex information ecology characterized by collaboration, dynamism, and constructiveness, due to their static, critical,and binary perspectives. This study introduces the new metaphor of the "information hive" to overcome the limitations of existing theoretical paradigms. [Method/process] The study first deconstructs the elements of the information hive,constructing a three-dimensional theoretical framework encompassing the boundary environment layer, the core mechanism layer, and the value precipitation layer. Subsequently, by comparing the differences between information cocoon and information hive in terms of spatial form, operating mechanism, subject relationship, and governance orientation,the study points out that information hive achieves a theoretical shift from a closed, static, and one-way warning perspective to an open, dynamic, and mutually constructive perspective. Furthermore, the study elaborates on the theoretical innovation and practical guidance value of the information hive from three aspects: theoretical expansion, governance paths, and practical mechanisms. [Result/conclusion] The study shows that the information hive, as a more inclusive metaphor, provides a new analytical path for understanding and shaping an open, healthy, and sustainable information cology in the algorithmic age.

[Purpose/significance] To address the limitations of existing disruptive technology identification methods,which primarily rely on static analysis and struggle to continuously characterize dynamic evolution or accurately quantify the direction of trajectory shifts and breakout rates, this study constructs a disruptive singularity identification computational model integrating Natural Language Processing (NLP) and geometric dynamics, aiming to provide quantitative intelligence support for technological foresight and strategic decision-making. [Method/process] Utilizing the pretrained SciBERT model and sliding window techniques, discrete patent texts are mapped into a continuous, dynamic semantic vector space to extract the technological centroid. Drawing upon physical kinematics theory, the tangential acceleration and normal evolutionary curvature of the centroid's trajectory are calculated to represent the short-term concentration of R&D resources and technological route mutations, respectively. Furthermore, by combining Kernel Density Estimation (KDE) with the long-tail characteristics of the data, an identification mechanism for disruptive singularities is established using the superposition of dual statistical extremes (Top10%) as the strict criterion. [Result/conclusion] Empirical research in the gene-editing domain demonstrates that this model successfully captured the disuptive singularities associated with three transformative paradigm shifts. Compared to traditional patent volume mutation models, it can identify key signals 2-3 years in advance; compared to static semantic novelty models, it effectively filters out the pseudo-mutation noise of conventional incremental innovations. This study achieves a methodological expansion in technological evolution analysis from static scientometrics to dynamic kinematic characterization, providing robust intelligence support for technological foresight and strategic decision-making.

[Purpose/significance] Systematically identifying the hierarchical structure and evolutionary patterns of users′ role cognition of Generative Artificial Intelligence (GenAI) in academic topic selection not only enriches the theoretical framework of human-AI collaboration but also offers implications for layered interaction design of GenAI and for cultivating students′ academic innovation capabilities in the AI for science era. [Method/process] Based on the Computers Are Social Actors theory and role theory framework, this study employed a longitudinal interpretative phenomenological analysis to track 13 university students′ interactions with GenAI and their cognitive experiences during academic topic selection over three months through 36 in-depth interviews. [Result/conclusion] This study, drawing on Bloom′s taxonomy, developed a multi-level 3M (Minion, Mate, and Mentor) role spectrum comprising four hierarchical layers of role type, role dimension, specific role, and role manifestation. Users′ role perceptions of GenAI exhibit complex diversity; GenAI′s role positioning undergoes dynamic evolution as research progresses, and users′ role perceptions of GenAI present three dynamic evolutionary patterns: elevation, expansion, and adaptation.

[Purpose/significance] The trend of age-related diseases manifesting at younger ages is becoming increasingly prominent. To address the issue of health information anxiety among young groups resulting from this trend, it is essential to investigate its underlying mechanisms. [Method/process] Drawing on the health belief model and the MOA (Motivation-Opportunity-Ability) theory, this study proposed 12 research hypotheses and constructed a model of the formation mechanism of health information anxiety among young groups. It then distributed a questionnaire survey,which yielded 401 valid responses. Finally, the study utilized AMOS software for data analysis and model testing. [Result/conclusion] Perceived susceptibility has a significant positive effect on health information anxiety among young groups, while self-efficacy exhibits a significant negative effect. In contrast, perceived severity and perceived barriers are not directly associated with health information anxiety. Perceived susceptibility fully mediates the relationship between health status and health information anxiety. Self- efficacy fully mediates the two parallel pathways linking health information quality and health information alienation to health information anxiety. Additionally, self-efficacy partially mediates the relationship between health information literacy and health information anxiety.

[Purpose/significance] With the vigorous development of the open science movement, uncovering its enhancement effect on the international academic discourse power of Chinese papers and conducting a multi-perspective evaluation of such discourse power holds practical significance for facilitating the efficient dissemination of Chinese papers within the open science system and enhancing their international academic discourse power. [Method/process]This paper employs Chinese management papers as the sample. It constructs an evaluation index system that encompasses four key dimensions: academic originality, academic influence, academic communication power, and academic leadership power, by integrating scientometric indicators, altmetrics, and full-text bibliometric indicators. Simultaneously, the study utilizes propensity score matching to assess the enhancement effect of the open science environment on the international academic discourse power of Chinese papers. This study adopts the propensity score matching method to evaluate the promotion effect of the open science environment on the international academic discourse power of Chinese papers. It further applies the CRITIC-BP and CRITIC-TOPSIS methods to assess such discourse power from single-dimensional and comprehensive perspectives under the open science environment, and finally conducts intelligent verification of the evaluation results via CNN. [Result/conclusion] The open science environment exerts distinct promotive effects on four key dimensions of Chinese papers. Specifically, the enhancing impacts on academic recognition level, international dissemination effectiveness, and disciplinary leadership are 11.4, 2.19, and 0.1, respectively. Significant correlations are identified both among the various dimensions of the international academic discourse power of Chinese papers and between each dimension and the comprehensive evaluation result. International academic discourse power in Q1 journal papers tends to be highly concentrated. For Chinese scholars' participation in international collaborative Q1 journal papers, supportive involvement is the main form, and their leading role remains relatively limited.

[Purpose/significance] Sharing co-ownership information on social platforms has become a common and inevitable phenomenon. Understanding the co-ownership information disclosure behavior of social platform users is crucial for enhancing their co-ownership information sharing experience and protecting collective privacy. [Method/process] Adopting the grounded theory method, this study conducts a comprehensive, systematic and in-depth investi⁃gation into the characteristics, contexts and occurrence mechanism of co-ownership information disclosure among so⁃cial platform users from a collective perspective via three-level theoretical coding. On this basis, it develops a model for the occurrence mechanism of social platform users' co-ownership information disclosure behavior, which encom⁃passes 9 main categories, 19 basic categories and 102 initial concepts. [Result/conclusion] This study found that the content characteristics, subject characteristics, and audience characteristics of co-ownership information can affect in⁃dividuals' perception of collective benefits and risks. At the same time, the collective benefit and risk perception of in⁃dividuals will directly and indirectly affect their disclosure behavior of co-ownership information through collective fair⁃ness assessment. In addition, the disclosure of co-ownership information by individuals is directly influenced by their perception of information ownership and collective privacy coping. Moreover, individual perception of information owner⁃ship and collective privacy coping can also moderate the impact of collective benefit and risk perception on co-ownership information disclosure behavior. The results enrich the theoretical framework and research system of individual informa⁃tion disclosure behavior, and provide practical insights for policy makers and social platform developers to enhance us⁃ers'co-ownershipinformationsharingexperience.

[Purpose/significance] Exploring multimodal evidence reorganisation and association patterns from a re⁃source management perspective facilitates the formation of an evidence-based logic loop of "evidence fragments—evi⁃dence chains—factual verification", driving the transformation of information services toward data-driven evidencebase dapproaches. [Method/process] First, following the digital humanities research process, this study proposes an evidence-based framework for Hanfu digital resources based on knowledge graphs. Second, this study ensures the reli⁃ability of the evidence-based process and the verifiability of results through four aspects: evidence extraction, evidence association, cross-modal evidence fusion, and multi-source mutual verification of conclusions. Finally, using Song Dy⁃nasty women's clothing resources as an example, this study conducts evidence chain construction and scenario applica⁃tions to validate the scientific validity and feasibility of the proposed evidence-based framework. [Result/conclusion]By systematically analysing the eight evidence categories, five evidence levels, and seven evidence association patterns in the evidence-based context of Hanfu literature, it can achieve a pathway integrating "data preparation—evidence or⁃ganisation—conclusion verification", thereby validating the scientific validity of the evidence-based framework for Hanfu digital resources mentioned in this paper. This provides methodological support for evidence-based research on cultural heritage resources, with Hanfu evidence-based research as a representative example.

[Purpose/significance] While Large Language Model (LLM) demonstrates substantial potential in academic peer review, its application has also sparked widespread controversy regarding academic ethics and norms. Focusing on reviewers, this study investigates the behavioral processes and mechanisms underlying their collaboration with LLMs in real-world workflows. [Method/process] This study conducted in-depth interviews with 20 reviewers experienced in LLM-assisted peer review and employed thematic analysis for data analysis. [Result/conclusion] This study develops a human-AI collaborative behavior model in the context of peer review, consisting of five elements: collaborative conditions, collaborative motivations, collaborative strategies, collaborative feedback evaluation, and collaborative adaptation. Reviewers' behavioral decisions are moderated by subjective norms and capability foundations, and are driv⁃en by both instrumental and avoidance motivations. Based on the level of cognitive offloading and the degree of impact on the final review opinion, task allocation strategies between reviewers and LLMs can be structured as a continuum of "assistance–augmentation–co-creation agent." This continuum is accompanied by risk management strategies to ensureda⁃tasecurity and information quality. The collaboration between reviewers and LLMs is a continuous process of learning and adaptation. Driven by collaborative feedback evaluation, reviewers make corresponding behavioral adaptations, including the enhancement of LLM usage skills and the adjustment of collaborative strategies. This behavioral model provides new insights into the elements, pathways, and boundaries of human-AI collaboration in the context of peer review tasks.

[Purpose/significance] This study aims to develop an automated prediction framework for identifying poten⁃tially high-impact papers by integrating multidimensional feature index with deep learning methods, enhancing the comprehensive characterization and accurate prediction of academic value and impact, thereby providing methodologi⁃cal support and technical pathways for optimizing research evaluation, allocating academic resources, and informing science and technology decision-making. [Method/process] In order to realize the accurate prediction of potentially high-impact papers in the literature of social science field, this study firstly constructs a three-dimensional feature in⁃dex system of scientific and technological papers from the three dimensions of the papers' own features, content fea⁃tures and cited features. Then, this study designs and realizes the automatic prediction framework of potentially highimpact papers, which integrates the three-dimensional feature index of papers and deep learning model, to realize the deep learning prediction of potentially high-impact papers in the massive literature from the major social sciences dis⁃cipline named economics. Finally, the differences and advantages in features of potentially high-impact papers are sys⁃tematically compared and analyzed. [Result/conclusion] There are significant differences among various dimensional index in scientific and technical literature. The constructed three-dimensional feature index system and feature vector space, combined with deep learning prediction models, can comprehensively measure the value and influence of scien⁃tific and technological papers. At the same time, the superior prediction effect can promote the automatic prediction and recommendation application of potentially high-impact papers in massive literature. Artificial neural networks and TabNet perform well in prediction accuracy and precision, but are inferior to traditional machine learning models in metrics such as recall, P-R area, and AUC value. Using citation features of papers outperforms using the paper's own features or content features to predict high impact papers. Potentially high-impact papers exhibit significant advantag⁃es in multidimensional feature index, including papers' own features, thematic features, citation dynamics, etc.

[Purpose/significance] This study aims to explore the construction of a group portrait of the motivation for organized research collaboration behavior among philosophy and social sciences researchers, so as to provide decision support for promoting organized research collaboration in this field. [Method/process] Firstly, through a three-stage interview method combining one-on-one interviews, focus groups, and expert consultations, and based on the self-de⁃termination theory, the motivations for collaboration behavior were identified. Subsequently, the K-means algorithm was used to cluster the survey data and construct the group portrait of the motivation for organized research collabora⁃tion behavior among philosophy and social sciences researchers. [Result/conclusion] The study identified nine types of collaboration motivations, including four types of intrinsic motivations (co-innovation motivation, problem-solving motivation, academic exchange motivation, and social service motivation) and five types of extrinsic motivations (policy response motivation, resource integration motivation, efficiency improvement motivation, profit orientation motivation,and talent cultivation motivation). Four types of group portraits were constructed: profit orientation dominant type, so⁃cial service dominant type, resource integration-co-innovation dual dominant type, and talent cultivation-academic ex⁃change dual dominant type. The study interpreted and analyzed these different group portraits and put forward corre⁃sponding strategic suggestions for promoting organized collaboration in the field of philosophy and social sciences.

[Purpose/significance] By constructing a multi-level driving model, this study systematically analyzes the
topological relationships and action pathways of driving factors influencing mobile short video users' digital detox be⁃
haviors, aiming to provide a research paradigm with both theoretical explanatory power and practical applicability for
the digital health ecosystem. [Method/process] First, grounded theory analysis was employed to identify key factors af⁃
fecting users' digital detox behaviors. Second, the ADSM was applied to categorize these factors hierarchically and ex⁃
plore their transmission paths, revealing interrelationships among them. Finally, the MICMAC algorithm was used to
classify the influencing factors into clusters and validate the effectiveness and reliability of the constructed model. [Re⁃
sult/conclusion] The results indicate that the counter-dependency model of mobile short video users' digital disen⁃
gagement behaviors can be divided into seven hierarchical levels. The twelve driving factors are further classified into
three clusters: dependent clusters, autonomous clusters, and driving clusters. Among them, self-regulation capability is
identified as the most fundamental driver influencing digital detox behaviors.

[Purpose/significance] Exploring quality improvement strategies adapted to the characteristics of data fac⁃tors from the perspective of multiple stakeholders, ensuring the circulation and application of high-quality data factors,thereby promoting the high-quality development of the data factor market. [Method/process] From the perspective of market self-governance, this study investigates the strategic choices of the data trading platforms, data suppliers, and data demanders under changing costs and benefits. By constructing an evolutionary game model, the study analyzes the interactions among the three stakeholders and their influencing factors. Finally, MATLAB software is used to simulate the dynamic process of strategy evolution under different conditions. [Result/conclusion] The active regulation of data trading platforms is influenced by both explicit and implicit benefits and regulatory costs. The decision-making of data suppliers regarding the provision of high-quality data depends on cost differentials as well as the platform's incentive and penalty mechanisms. The feedback costs and incentive intensity are key factors affecting the feedback behavior of data demanders. Effective platform regulation, a reasonable incentive and penalty mechanism, and the feedback from data demanders together contribute to a virtuous cycle that promotes the circulation of high-quality data elements.

[Purpose/significance] In response to the strategic demand for building autonomous disciplinary knowl⁃edge systems, this study aims to systematically review the research status and evolutionary trajectory of digital library user experience (UX). The goal is to construct a knowledge system for this specific field, thereby providing a micro-lev⁃el foundation and a practical case for the development of the parent discipline's autonomous knowledge system. [Meth⁃od/process] Following the PRISMA guidelines, a systematic literature review was conducted. A total of 81 documents were selected as the analytical sample from three major Chinese databases. The included literature was systematically coded for research themes, fundamental concepts, research paradigms, theoretical models, and evaluation frameworks.[Result/conclusion] This study identifies a three-stage developmental trajectory in digital library UX research and proposes a three-dimensional analytical framework for its research paradigms. Furthermore, it constructs a comprehen⁃sive knowledge system for digital library UX, centered on the core path of "value-driven - status analysis - practical approach". This work offers a novel pathway for the construction of disciplinary knowledge systems.

[Purpose/significance] In the context of sudden emergency events, content on social media poses severe challenges to online environments and emergency management due to the amplification effect of public opinion dissemi⁃nation. Traditional detection methods suffer from issues such as imbalanced classification data and low detection accu⁃racy, necessitating efficient solutions. [Method/process] This study constructs a dedicated dataset of content from Wei⁃bo comments in sudden emergency events. Generative Artificial Intelligence (GenAI) technology is employed to gener⁃ate semantically equivalent pseudo-toxic content through few-shot prompt learning, balancing the sample distribution.Furthermore, the MACBert-Att model is proposed by integrating the MACBert pre-trained model with a global atten⁃tion mechanism, enhancing the semantic capture capability for domain-specific terms and emotional expressions. [Re⁃sult/conclusion] Experiments demonstrate that with GenAI data augmentation, the MACBert-Att model achieves an F1 score of 0.95, representing a 15% improvement over the baseline Bert model and significantly outperforming tradi⁃tional augmentation methods like SMOTE. This validates the collaborative effectiveness of GenAI-based semantic-lev⁃el data augmentation and the model architecture.

[Purpose/significance] Currently, under the strategic background of accelerating the construction of an in⁃dependent knowledge system for Chinese philosophy and social sciences, the construction and research of classic litera⁃ture has highlighted unprecedented urgency and practical significance. [Method/process] This paper, based on the theoretical perspectives of information resource management, education, and other disciplines, comprehensively utiliz⁃es research methods such as literature surveys and comparative analysis to deeply explore the conceptual connotation,main characteristics, and originality of the concept of "classic literature". [Result/conclusion] The "classic literature" comprises mainstream, classic, and essential reading materials from various disciplines, selected by the academic com⁃munity based on scientific standards and standardized procedures. This selection, along with the subsequent compilation and processing of these materials into a reading tool and reference system, possesses nine key characteristics: timeli⁃ness, consensus, leadership, historical relevance, and originality. Addressing the strategic need to construct an indepen⁃dent knowledge system for China, and grounded in the intellectual soil of Chinese philosophy and social sciences, the "classic literature" has refined new terminology, endowed it with new connotations, derived new conclusions, formed new advantages, and achieved new expansions, thus fulfilling the characteristics of an original academic concept.

[Purpose/significance] The rapid development of Generative Artificial Intelligence (GAI) technology, cou⁃pled with intensifying technological competition between China and the United States, has posed severe challenges to user privacy and data security. The study compares the privacy policies of GAI platforms in both countries, aiming to analyze their characteristics and differences, and provide useful insights for the sustainable and healthy development of AI applications in China. [Method/process] The study selects 20 representative privacy policy texts from GAI plat⁃forms in China and the U.S., employing LDA topic modeling and the PMC index model for quantitative analysis.Through topic extraction and consistency evaluation, it compares the strengths and features of privacy policies in both countries to inform policy optimization in China. [Result/conclusion] The findings show that Chinese platform policies
emphasize government-led design and full-process regulation, while U.S. policies focus more on market orientation, us⁃er autonomy, and legal remedies. Based on this, the study suggests that Chinese AI platforms should shift from a "defen⁃sive compliance" approach to "proactive rule-making", strengthen data lifecycle management, build a sound regulatory framework, and improve mechanisms for protecting user rights and addressing complaints, thereby enhancing the effec⁃tiveness of privacy governance and platform competitiveness.

[Purpose/significance] The conceptual connotation of the information cocoon, which is both tangible and in⁃tangible, has led to the bottleneck of inconsistent research conclusions and inability to integrate research results in quantitative methods based on conceptual descriptions. Therefore, abstracting the concept of information cocoon and gradually improving the quantitative methodology system becomes a reasonable choice that conforms to the laws of sci⁃entific development. [Method/process] Starting from the important issue of analyzing the form of information cocoons,this article reviews and summarizes the research results at home and abroad. From the perspectives of metaphorical con⁃cept setting, the possibility of multidimensional forms, ecological fallacies and reductionism in empirical research, and the inability to track the "behavior" of social media users, the reasons for the "formal but intangible" nature of informa⁃tion cocoons and related issues in the field are sorted out, and the necessity of abstracting the concept of information co⁃coons is explained. On this basis, based on the process characteristics of information cocoon formation, a conceptual ab⁃stract model of information cocoon is constructed using complex network domain knowledge, and the method of quanti⁃fying information cocoon through complex network domain knowledge is interpreted from three levels: selection homog⁃enization, content homogenization, and group homogenization. [Result/conclusion] This conceptual abstract model aims to innovatively achieve the first abstraction of the concept of information cocoon based on the connotation of the concept of "being tangible but intangible", forming a scientific, stable, and strongly generalized quantitative method for information cocoon.

[Purpose/significance] With the diversification of online platforms, users are increasingly inclined to carry out information exchange activities across multiple platforms, therefore, focusing on cross-platform users, exploring their interaction types, identifying high-value interactions and analyzing the path of their formation are of great signifi⁃cance in understanding cross-platform users' information exchange behaviors. [Method/process] By aligning bloggers who have cross-platform interactions on representative social sharing platforms and short-video platforms, and obtain⁃ing all comments under the content they posted throughout the year of 2023, we implement the division of interaction types based on the K-means method to identify high-value interactions and analyze their formation paths. [Result/con⁃clusion] Two-way interactions between cross-platform bloggers and audiences can be divided into two categories: ac⁃tive interactions and loose interactions, both of which appear more often on the social sharing platforms. Active interac⁃tions are more frequent, involve a wider range of topics, and have a more intimate relationship than loose interactions,
which helps both parties to form social capital, and can be regarded as an online extension of interpersonal relation⁃ships, and is a high-value interaction. The formation of such high-value interaction needs to go through three stages:one-way attention, surface contact and mutual involvement, and there are three main formation paths: approval-contri⁃bution type, demand-exchange type and care-sharing type, among which approval-contribution type is the type that appears most frequently on social sharing platforms, demand-exchange type is the type that occurs the most on shortvideo platforms, and care-sharing type is the type that occurs less often on both platforms.

[Purpose/significance] In the context of digital information, the social function of information resource man⁃agement has changed from information resource provider to knowledge service innovator, social problem solver and cul⁃tural ecology builder, and the user information acquisition paradigm has changed from "search - screening" to "ques⁃tion - generation". Generative artificial intelligence represented by DeepSeek, as a new channel for information acqui⁃sition, is gradually regarded by users as an information service model similar to information resource management.[Method/process] Focusing on the phenomenon of the formation and exertion of the information resource management function of generative artificial intelligence, exploring its causes from multiple dimensions such as technology, society and cognition, analyzing the social benefits of generative artificial intelligence as an information channel under techno⁃logical advantages, and clarifying the resulting technology intermediary cognition and its alienation challenges. [Result/
conclusion] Explored the construction strategies of the human-machine collaborative cognitive ecosystem, provided references for solving the challenges brought by generative artificial intelligence in performing the function of informa⁃tion resource management and developing digital intelligence-driven information services.

[Purpose/significance] As the frontier of data utilization, the intelligence analysis still faces challenges such as decoupling of thinking and technology, the emergence of marginal effects, and poor adaptation between tasks and technologies in the fusion of multi-source and multi-modal data. It is urgent to explore the logical thinking and technical path for data fusion and utilization under the guidance of intelligence thinking and the traction of intelligence tasks. [Method/process] This paper clarifies the ubiquity, high dimensionality, dynamicity, complementarity and re⁃dundancy characteristics of multi-source and multi-modal data, and explains the multi-source and multi-modal data fusion for intelligence analysis from the perspective of information chain and information theory; based on the principle of "data is useful, available and utility", the underlying logic is constructed to form the basic idea of organic coordina⁃tion of intelligence tasks, data characteristics and system effectiveness; by embedding digital intelligence technology in⁃to the intelligence analysis process, a three-stage progressive multi-source and multi-modal data fusion technical path of "data resource preparation, multi-dimensional feature alignment, smart intelligence generation" is proposed. [Re⁃sult/conclusion] The research results provide strategic references and methodological guidelines for the fusion of multi-source and multi-modal data to empower intelligence practice, which is helpful to promote the deep mining of da⁃ta value and the digital-intelligent transformation of intelligence work.

[Purpose/significance] Frequent privacy leakage incidents and increasingly complex privacy control strate⁃gies have exacerbated user privacy fatigue, and exploring the formation mechanism of social media users' privacy fa⁃tigue can provide a theoretical reference for user cognitive load relief, privacy anxiety debugging and effective privacy protection strategy formulation. [Method/process] Combining cognitive load, cognitive bias and CAC theory, the forma⁃tion mechanism model of privacy fatigue of social media users was constructed, and the partial least squares structural equation model and fuzzy set qualitative comparative analysis method were used for empirical test and configuration analysis. [Result/conclusion] In terms of cognitive factors, overconfidence positively affects optimism bias, informa⁃tion overload, social overload and system function overload all positively affect emotional exhaustion, and system func⁃tion overload also has a positive impact on negative neglect. In terms of emotional factors, emotional exhaustion, nega⁃tive neglect and optimistic bias all positively affect user privacy fatigue, and optimistic bias not only has a positive im⁃pact on emotional exhaustion and negative neglect, but also positively affects privacy fatigue under the mediating effect of the two. In terms of configuration effect, the two antecedent configurations of information overload and social over⁃load will trigger user privacy fatigue. In addition, there was no significant difference in the effect of control variables such as demographics on social media users' privacy fatigue.

[Purpose/significance] With the rise of generative AI, its role as a social actor is reshaping the process of in⁃formation acquisition. Against the backdrop of the uncertainty arising from human to human proxy search, exploring the influence of uncertainty on human to machine proxy search behavior from the perspectives of positive and negative emo⁃tions is of great significance for the further development of this technology. [Method/process] Based on the motivation⁃al information management theory, a research model was constructed, and a total of 13 research hypotheses were pro⁃posed. The data were collected using the questionnaire survey method and the critical incident technique. The relevant model calculations were carried out through the partial least squares structural equation modeling. [Result/conclu⁃sion] This study first reveals the correlation between the uncertainty differences, the low matching between information and tasks, and individual emotions. That is, an increase in uncertainty differences enhances individuals' negative emo⁃
tions and, correspondingly, reduces positive emotions. Under the influence of negative emotions, users lower their eval⁃uation of expected outcomes and effectiveness, thereby suppressing human to machine proxy search behavior. Although the low matching between information and tasks has no significant impact on the emotional state, it positively promotes human to machine proxy search behavior. Emotions have no significant impact on the results of human to machine proxy search behavior.

[Purpose/significance] Currently, the scientific research big data is a major concern for the economic, so⁃cial, and technological development of various countries. Exploring its basic framework and correlative logic holds sig⁃nificant theoretical and practical value. [Method/process] This study grounded in a comprehensive review of the perti⁃nent theoretical foundations of the basic framework system for scientific research big data, transcends ecological and cy⁃bernetics theories and methods. Following systematic principles, it establishes the basic framework system for scientific research big data based on the concepts of "nascence-symbiosis-regeneration-parasitism". The basic framework sys⁃tem adheres to ecological principles. It considers "nascence" as the foundation, "symbiosis" as the guarantee, "regener⁃ation" as the key, and "parasitism" as a special case，employing process logic, time logic, functional logic, and formal logic as the logical domains, facilitating cyclical feedback and iterative optimization. [Result/conclusion] The theoreti⁃cal construction of the basic framework system for scientific research big data provides a novel perspective for the scien⁃tific research big data study, offering valuable guidance for the organization and management of the scientific research big data. In practical terms, the basic framework system for scientific research big data aids in the analysis, scrutiny,and mitigation of negative phenomena such as data monopolies, data fragmentation and data dormancy. This contribu⁃tion supports the establishment of a robust ecosystem of scientific research big data, fosters a sense of shared destiny within the scientific research big data community, revitalizes the vitality of research data, and establishes a firm founda⁃tion for harmonious symbiosis and mutually beneficial development among research entities at different levels.

[Purpose/significance] Investigating the elements and mechanisms of digital detox behavior of mobile so⁃cial media users can help to enrich the information behavior model, provide theoretical and applied guidance for the indepth analysis of the digital detox behavior of mobile social media users, and ensure a healthy balance between the us⁃ers' normal life and the time they spend on digital devices. [Method/process] Based on the CAPS theoretical model framework, we analyze the elements of the digital detox behavior of mobile social media users from the four dimensions of "context-cognitive-emotional-behavioral", and we use the key event technology method to deeply excavate the in⁃trinsic mechanism of the behavior, and construct the mechanism-relationship model of the digital detox behavior of mo⁃bile social media users. model. [Result/conclusion] The mechanism of digital detox behavior of mobile social media us⁃
ers can be divided into four sub-mechanisms: situation-driven, cognitive connection, emotional regulation and behav⁃ioral response. The management strategies for digital detox behavior of mobile social media users include four aspects:seeking alternative activities and building real-life scenarios; taking the initiative instead of being passive and enhanc⁃ing self-awareness; establishing incentive mechanisms and strengthening positive emotions; using device functions torestrain behavioral habits.

[Purpose/significance] As“AI for Science”is increasingly viewed as the“fifth paradigm”of scientific re⁃search, Artificial Intelligence (AI) has begun to exert a profound impact on both the research processes and disciplinary boundaries of Information Resources Management (IRM). However, a comprehensive examination of AI’s applications and influences in IRM, from a holistic disciplinary perspective using systematic theoretical tools, remains lacking.Drawing upon the I-model theory, this paper investigates the key elements, underlying mechanisms, and future chal⁃lenges of AI-empowered IRM research, aiming to offer fresh perspectives for both academic inquiry and practice. [Method/process] Employing a literature review and representative case analyses, this study summarizes the core com⁃ponents and key pathways of AI-driven Information Resources Management (AI4IRM). It then utilizes the four dimen⁃sions of the I-model—information, technology, people, and organization/society—to analyze AI’s innovative applica⁃
tions in tasks such as information retrieval, knowledge discovery and management, as well as data processing and analy⁃sis. The paper also provides a comprehensive review of forthcoming trends and potential risks. [Result/conclusion]The findings reveal that AI holds substantial promise in enhancing information analysis accuracy, decision-support effi⁃ciency, and knowledge service capabilities. Nevertheless, significant challenges persist, particularly regarding data quality in large-scale datasets, algorithmic bias, privacy concerns, and ethical regulations. By illuminating the dynamic relationships and complexities inherent in AI-driven IRM, the I-model theory furnishes theoretical and practical foun⁃dations for advancing interdisciplinary research and informed decision-making in this evolving field.

[Purpose/significance] With the rapid advancement of generative AI technologies, prompts have transcend⁃ed their traditional role as mere human-computer interaction interfaces and have evolved into a core competency for navigating the digital age. [Method/process] This study systematically investigates the concept of prompt literacy, tak⁃ing the multifaceted nature of prompts as a logical starting point. Drawing on relevant perspectives from the school of so⁃cial informatics, it explores the theoretical underpinnings of prompt literacy in three dimensions—individual, system,and practice. It also explores practical approaches to improving prompt literacy across society from an interdisciplinary perspective. [Result/conclusion] As a crucial link between human-AI interaction and value co-creation, prompt liter⁃acy exhibits a dual character, encompassing both information practices and technological ethics. There is an urgent
need to develop a theoretical framework rooted in socio-technical practices. This study outlines several directions for future research, aiming to advance both the theoretical refinement and practical development of prompt literacy.

 [Purpose/significance] Short videos have gradually become one of the primary channels for the public to ob⁃tain information. Through short videos, various types of information can rapidly disseminate among users, including a large number of false short videos. Many of false short videos are adopted or shared by users, it may harm them and dis⁃turb the online information environment. Therefore, this study explores the factors influencing the dissemination of false short videos, in order to provide references for the governance of false short video and the optimization of online in⁃formation ecology. [Method/process] To explore the influence of content sentiment of false short video on user emotion and false short video dissemination effect, this study employed user sentiment as a mediator and content relevance as a moderator, constructed the research model based on the CAC Model and ELM Model, and then collected 1007 effective false short videos from the TikTok, extracted image, text and audio sentiment from these false short videos based on text
sentiment analysis, image recognition, and other technologies. As such, the study verified the moderating effect of con⁃tent relevance. [Result/conclusion] Images, text and audio sentiment of false short videos exert significant positive ef⁃fects on users' emotions in different degrees, while the author's avatar and background music exert no significant effect on users' emotions. User emotion exerts a significant negative effect on dissemination effect of false short videos, and partly plays a mediating role between the content sentiment of false short video and its dissemination effect. Content rel⁃evance significantly moderates the influence of the negative impact of user emotion on false short video dissemination effect.

[Purpose/significance] Managing sensory information resources in cultural heritage and creating sensory interactive experiences are crucial pathways for cultural heritage conservation and revitalization in the digital age. Com⁃pared to vision and hearing, the sense of smell is a dimension that has not been fully recognized. Revealing olfactory in⁃formation in cultural heritage activation and achieving olfactory reconstruction of cultural heritage is an important and exploratory frontier research. [Method/process] Based on comprehensive literature review, this paper proposes core concepts such as cultural heritage smellscape and its information revelation and digital construction using interdisci⁃plinary theories, methods, and perspectives. Building upon these concepts, an overarching framework for the informa⁃tion revelation and digital construction of smellscape in cultural heritage, relying on the discipline of information re⁃source management, is developed. [Result/conclusion] This framework, with cultural heritage activation as the strate⁃
gic background, centers on olfactory information resources and management, with digitization as the mainline, consist⁃ing of two stages: digital preparation (information revelation) and digital implementation (digital construction), and five components: olfactory information collection, olfactory information organization, olfactory information presentation, ol⁃factory information foundational applications, and olfactory information comprehensive applications. Corresponding to the two stages in the framework, the paper further elaborates on two critical issues: the representation of olfactory infor⁃mation in cultural heritage and olfactory information interaction, and outlines an interdisciplinary methodological sys⁃tem for addressing these issues.

[Purpose/significance] To effectively respond to diversified, concealed, high-intensity, large-scale, and persistent cyberspace security threats, it is imperative to construct an integrated, comprehensive, and intelligent cyber⁃space security intelligence service architecture. [Method/process] Using the enhanced Hall model as the analytical framework and adhering to the principles of centering on cyberspace security and security intelligence as a service, this study constructs a cyberspace security intelligence service architecture and deeply analyzes its operating mechanism.[Result/conclusion] The cyberspace security intelligence service architecture consists of three components: process submodule, logic submodule, and cognition submodule. The service processes cater to three types of demands: cyber⁃space security perception, cyberspace security judgment, and cyberspace security execution. The service work reflects the operating logic of " attack scenario traceability-event correlation identification-crisis response escalation". The
cognition of cyberspace security intelligence services is mainly embodied in three aspects: value consensus, technical support, and capability guarantee. The construction of the cyberspace security intelligence service architecture can pro⁃ vide a reference for intelligence service work in China's cyberspace security guarantee system.

[Purpose/significance] While AIGC strongly empowers scientific research knowledge production, it also triggers traditional academic misconduct risks such as plagiarism, fabrication and falsification, giving rise to new risk patterns such as AI ghostwriting and AI technology limited academic misconduct, which will impact on the current sci⁃entific research evaluation system. It is necessary to trace the root causes of these risks and regulate them. [Method/process] By deconstructing the underlying logic of AIGC intervention in knowledge production, it can be seen that Gen⁃erative AI acts on the knowledge level evolution path of "data-information-knowledge", as well as the thinking opera⁃tion process of knowledge search and evaluation, resulting in diverse types of Human-AI collaboration relationships,such as AI dominant, AI cooperative and AI assisted. The academic misconduct responsibilities of researchers vary un⁃der different types of Human- AI collaboration. [Result/conclusion] In terms of specific responsibility regulation
schemes, the traditional objective accountability stance is difficult to cope with the problem of "identity mismatch" be⁃tween AIGC academic misconduct subjects and responsible subjects. In this regard, the subjective accountability stance should be adopted, and set differentiated academic normative obligations such as originality declaration, trans⁃parency obligation, falsification prohibition, viewpoint tracing and factual examination for the author based on their "participation" and "contribution" in knowledge production. The author's subjective fault and its condemnability could be further evaluated by the nature and degree of obligation violations, thereby achieving a reasonable construction of the AIGC academic misconduct risk responsibility system.