[Purpose/significance] This study aims to clarify the core impact factors and pathways in data value realiza⁃tion, thereby promoting the effective release and realization of data value. [Method/process] A multi-chain system for data value realization is constructed. The CFCS-DEMATEL method is employed to identify causal relationships among factors, and fuzzy cognitive maps are used to simulate single-chain, double-chain, and triple-chain models to identify core factors within and across chains and analyze key impact paths. [Result/conclusion] A single-chain provides limit⁃ed momentum; a double-chain improves data flow efficiency; and triple-chain collaboration significantly enhances val⁃ue realization performance. The core impact factors differ across the single-, double-, and triple-chain models, and the impact paths within and across chains vary with the stage of data flow. Data value realization can be improved by strengthening single-chain capabilities, enhancing cross-chain collaboration, and advancing triple-chain collabora⁃tive governance.

[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] Aiming at the problems of single feature dimension and insufficient interpretability in the early identification of current breakthrough papers, this study constructs an interpretable machine learning meth⁃od to improve the identification accuracy and logical transparency, providing methodological support for scientific re⁃search management and innovation layout. [Method/process] Firstly, starting from the connotation of breakthrough pa⁃pers, on the basis of traditional features, innovation attribute measurement is introduced, knowledge innovation vectors are obtained by inducing large language Prompts, and a multi-dimensional feature system of breakthrough papers is constructed; secondly, an information expression system is built based on formal concept analysis (FCA), core features are screened by combining statistical correlation analysis and FCA attribute reduction algorithm, and various machine learning classifiers are used to predict the model identification effect; finally, a two-layer interpretation framework based on FCA concept lattice and SHAP analysis is constructed to form a visual interpretation chain from screening rules to prediction verification. [Result/conclusion] XGBoost model has an F1 value of 0.952 on multi-disciplinary da⁃tasets, which is significantly better than traditional methods; the two-layer interpretation system clarifies the feature combination rules of high innovation attributes and strong knowledge correlation, and quantifies the contribution of sin⁃gle features to the prediction results.

[Purpose/significance] This study aims to clarify the triggering mechanism of bystander users' willingness to intervene with Internet illegal and malicious information, so as to provide beneficial reference for the concrete imple⁃mentation path of online information content ecological governance. [Method/process] This study employs the experi⁃mental vignette methodology, introducing the classic bystander intervention model into the context of online illegal and harmful information. It constructs a formation path of bystanders' intervention intention, and further incorporates the protection motivation theory and information content ecological literacy to explore the boundary conditions of this path.[Result/conclusion] The "affective attention → social hazard perception → sense of online responsibility → self-effi⁃cacy" path of bystander intervention intention is applicable in three kinds online illegal and harmful information scenar⁃ios. At the same time, threat appraisal plays a paradoxical "risk incentive" role in bystander intervention contexts, gen⁃erally strengthening the influence of self-efficacy on intervention intention. Outcome expectancy significantly and nega⁃tively moderates the relationship between self-efficacy and intervention intention in the contexts of cyberbullying infor⁃mation and malicious ideological information. Moreover, information content ecological literacy significantly and nega⁃tively moderates the relationship between self-efficacy and intervention intention only in the context of malicious ideo⁃logical information.

[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] Artificial intelligence is profoundly driving the paradigm transformation, content op⁃timization, and efficiency leap of public digital cultural services. By constructing vertical domain AI agents, an attempt is made to provide a feasible path for enhancing the efficiency of public digital cultural services through the empower⁃ment of artificial intelligence. [Method/process] Point out the specific aspects of how vertical domain AI agents em⁃power the efficiency improvement of public digital cultural services, design the construction path of vertical domain AI agents for public digital cultural services, and conduct empirical tests on the proposed path by taking the construction of vertical domain AI agents for digital cultural services at the 2025 Asian Winter Games in Harbin as an example. [Re⁃sult/conclusion] Through empirical tests, it is proved that the construction path and application scenarios of the verti⁃cal domain AI agent for public digital cultural services proposed in this study are feasible, thereby exploring an effec⁃tive solution for enhancing the efficiency of public digital cultural services through artificial intelligence based on the construction of vertical domain AI agents.

[Purpose/significance] This study aims to systematically integrate and theoretically analyze practical expe⁃riences in infodemic management. It seeks to enrich the theoretical landscape and optimize the efficacy of practical in⁃terventions. [Method / process] A web-based survey method was employed to collect infodemic management initia⁃tives implemented by major international bodies, including UNESCO, the World Health Organization and the Europe⁃an Commission. Utilizing qualitative research methodologies, these beneficial practices were systematically integrated following a rigorous procedure of "material selection, discretization, and item clustering" to extract management strate⁃gies for infodemics. These strategies underwent theoretical analysis to elucidate their application principles. Guided by these principles, a two-dimensional "stage-countermeasure" matrix was adopted as an analytical framework to dissect the application methods of these strategies. [Result/conclusion] The study proposes a systematic response framework for infodemics comprising "management strategies-application principles-application methods". The management strategies are categorized into four dimensions: strategies targeting information source elements, information content ele⁃ments, information receiver elements, and environmental optimization. The application of these strategies should adhere to principles such as contextual adaptability, curve flattening, rapid response, proactive prevention, sustained effort, and collaborative synergy. Furthermore, it is imperative to align strategies with the evolutionary stages of the infodemic, prior⁃itizing distinct combinations of strategies based on the specific characteristics of each stage.

[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] Exploring the constituent elements of a multimodal holographic knowledge profile architecture, constructing its theoretical model, and elucidating its operational mechanisms to provide a theoretical  framework and directional guidance for the transformation of intelligent knowledge services within the human-AI interaction paradigm. [Method/process] Based on the structural logic and process logic of human-AI knowledge interaction, this study delves into the constituent elements of a multimodal holographic knowledge profile architecture. Building upon this foundation, it constructs a theoretical model for the architecture, elucidates its operational mechanisms,and analyzes its practical feasibility and value realization across various application domains. [Result/conclusion] In human-AI interaction scenarios, the multimodal holographic knowledge profile architecture comprises static and dynamic components. Static elements include four core components: subject, resources, space, and technology. Dynamic elements encompass five key components: demand mapping, scenario embedding, memory reconstruction, value reshaping, and service coordination. The multimodal holographic knowledge profile architecture is structured across three hierarchical levels: the foundational construction layer, the interactive perception layer, and the value service layer. The three levels undergo cyclical iteration. Through the knowledge twin system, they establish a contextualized knowledge unit generation mechanism, an asset-based knowledge profile storage mechanism, and a collaborative knowledge service feedback mechanism. Together, they shape a one-stop intelligent knowledge service system encompassing "demand-service-feedback-optimization", driving the intelligent transformation and upgrading of human-AI interactive knowledge services.

[Purpose/significance] This research investigates the criticalfactors influencing emergency intelligence services to clarify their hierarchical structure and interrelated pathways. The ultimate goal is to offer both a theoretical foundation and actionable guidance for improving the efficacy of government emergency management. [Method/process] Based on the methodology of the WSR system, an index framework for the influencing factors of emergency intelligence services has been developed, encompassing three dimensions: Wuli, Shili, and Renli. Through the application of the fuzzy DANP methodology, this study elucidates the complex interrelationships and hierarchical significance among various influencing factors, ultimately pinpointing the key determinants in emergency intelligence service systems. Using the ISM framework, the influencing factors are stratified into a hierarchical structure, followed by a path-dependent correlation analysis to uncover their causal interlinkages. [Result/conclusion] The effectiveness of emergency intelligence services stems from the dynamic interaction of four hierarchical dimensions: surface phenomena, shallow-layer factors, mid-level systems, and foundational determinants. Among them, identifies information infrastructure, security mechanisms, digital technologies, and policy-regulatory frameworks as foundational determinants, whereas data standardization systems,participation willingness, demand preferences, psychological resilience, and risk perception constitute proximal influencing factors.

[Purpose/significance] The information cocoon is a stubborn ailment that must be cracked in the digital era. Information acquisition through generative artificial intelligence can significantly enhance users' information-collection efficiency and help expand information; yet it also harbors the risk of information narrowing, which may lead to cognitive closure. Its latent double-edged-sword effect urgently demands systematic exploration. [Method/process] From an individual perspective, based on cognitive-offloading theory and integrating the elaboration likelihood model with dual-process theory, this study proposes the positive and negative effect paths of "breaking the cocoon" and "spinning the cocoon", constructs a model according to theoretical hypotheses, and conducts empirical testing using PLS-SEM. [Result / conclusion] Generative artificial intelligence information acquisition presents a double-edged-sword effect on information cocoons; the cognitive offloading triggered by generative artificial intelligence information acquisition can, by stimulating users' self-initiated behavior, broaden information boundaries and break cocoons, yet it can also, by fostering behavioral inertia, exacerbate information narrowing and create cocoons; critical thinking, as a key moderating variable, can weaken the negative effect and curb cocoon formation.