[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] Reveal the generation mechanism of privacy leakage risks among users of medical e-commerce platforms and propose corresponding control strategies to enhance user privacy security in the medical ecommerce sector and safeguard user privacy rights. [Method/process] In-depth interview is conducted to collect ini⁃tial data. Using thematic analysis, the MOA and SOR frameworks are integrated to extract the influencing factors of pri⁃vacy leakage risks in medical e-commerce. The interrelations among these factors are mapped out, and a model for the generation and control mechanism of these risks is constructed. [Result/conclusion] Privacy disclosure motivation, pri⁃vacy protection environment, and privacy literacy, as stimuli, all indirectly influence users' privacy risk control behav⁃iors through the mediation of perceived privacy leakage risk. Furthermore, users' privacy cognitive psychology impacts their perception of privacy leakage risk, moderating the relationship between stimuli and perceived privacy leakage risk, as well as between perceived risk and risk control behaviors. Given this, the research proposes a range of data gov⁃ernance and privacy protection strategies from the dual perspectives of government and platform, including establishing a full lifecycle privacy protection mechanism and implementing graded labeling of privacy information.

[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.

编者按：2025年底，中国人民大学书报资料中心信息资源管理系列刊、图情档39青年学者沙龙与黑龙江大学信息管理学院共同发起了“2025年度中国信息资源管理学界学术‘热点’与‘冷点’”评选活动。40余家专业期刊媒体联合发布了本年度信息资源管理领域的学术“热点”与“亟待加强的研究议题”（“冷点”）评选结果。该活动围绕本领域年度学术前沿动态与潜在议题展开系统梳理与评议——其中，“热点”聚焦于领域内受到集中关注的研究趋势，而“冷点”则指向那些值得深入研究，但未引起学界充分重视，或未能深入研究的涉及知识体系建构的学科基础、深层问题，或社会实践需要理论支撑、解决方案的重要议题。“热点”彰显时代关切，“冷点”关乎学科根基，“冷”“热”交融共进，共同为中国自主的信息资源管理知识体系建构注入新动能与新活力。

[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] It is of great value to grasp the concept of government behavior and governance ac⁃curately, based on the dictionary method to construct a fine grain analysis of Chinese policy texts suitable for the open data environment. [Method/process] The words that reflect emotional intensity in policy texts are defined as tendency words, and their structural characteristics and semantic relevance are used to construct a tendency dictionary. First, the seed words are extracted according to the interpretation opinions of domain experts and combined with the point mutual information algorithm to expand the dictionary online. Secondly, based on the theory of formal concept analysis, the top⁃ic connotation of policy texts is defined and quantified, the hierarchical relationship between policy topics is mapped to the semantic relationship between words, and the synonymous tendency words with topic similarity are screened. Final⁃ly, the credibility and validity methods are used for empirical testing. [Result/conclusion] The propensity dictionary
has high accuracy and recall rate in the task of policy text emotion recognition, which is suitable for large-scale fine grain policy text analysis, and provides a reliable and novel quantitative tool for policy research and decision-making.

[Purpose/significance] After experiencing unprecedented user growth, generative AI has encountered a sig⁃nificant decline in user scale for the first time. Identifying and analyzing the key factors influencing generative AI user discontinuance behavioris of great significance for optimizing user experience, enhancing user stickiness, and promot⁃ing the healthy development of the generative AI industry. [Method/process] This study used a combination of litera⁃ture survey and Delphi methods to systematically and comprehensively distill the influencing factors on discontinuance behavior among generative AI users, and identified and analyzed the key influencing factors using the DEMATEL meth⁃od. [Result/conclusion] The research findings indicate that: the discontinuance behavior of generative AI users is in⁃fluenced by 20 influencing factors in four dimensions: user factors, information factors, platform factors, and environ⁃mental factors; algorithmic literacy, degree of intelligence, degree of anthropomorphism, competitive environment and other 10 influencing factors are all key influencing factors of generative AI users′ discontinuance behavior; generative AI platform managers should reduce the occurrence of users′discontinuance behavior by deeply understanding users′ needs, improving users′ algorithmic literacy, establishing a social interaction platform and other aspects.

[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] The human-AI search context has significantly reshaped behavioral interactions and learning effect in learning-related search. This study aims to provide a comprehensive evaluation of learning effect and to examine how search tools and task types jointly influence learning outcomes. [Method/process] An experimen⁃tal method was adopted to design three types of learning-related search tasks: receptive, critical, and creative. Two kinds of search tools, traditional search engines and Generative AI (GenAI), were provided. Learning effect data were collected from users who completed different tasks with these tools. The analysis of learning effect was conducted from two perspectives: subjective learning experiences and objective learning outcomes.[Result/conclusion] Users′ learn⁃ing experiences were mainly influenced by task types, while the specific effect of the search tool used remained un⁃clear. In terms of learning outcomes, traditional search engines outperformed GenAI in receptive and critical tasks, par⁃ticularly in the number, coverage, and uniqueness of knowledge points as well as the depth of knowledge facets. For cre⁃ative tasks, however, both tools provided relatively limited support, with GenAI showing a slight advantage.

[Purpose/significance] Researching the adoption of government open data user information is conducive to enhancing the readability and accessibility of data, triggering users to focus their attention, and providing theoretical references for in-depth data value mining. [Method/process] Using the SOR (Stimulus-Organism-Response) frame⁃work as the analytical structure, and integrating the theories of narrative transportation, perceived value, trust, and at⁃tention-based view, this study analyzes narrative elements such as narrative content, structure, and data presentation methods, explores the moderating effect of attention allocation concentration, constructs a theoretical hypothesis model of factors influencing the willingness of government open data user information adoption, and empirically verifies the model through a survey of 572 government open data users. [Result/conclusion] The study shows that the narrative content, structure, and presentation methods of government open data positively affect cognitive trust and perceived val⁃ue, and perceived value positively affects cognitive trust. Both cognitive trust and perceived value positively affect user
information adoption willingness and play a mediating role in the process of influencing adoption willingness through the stimulus dimension. Attention allocation concentration positively moderates the impact of cognitive trust and per⁃ceived value on user information adoption willingness.

[Purpose/significance] The phenomenon of involution alters the individual's cognition of competitive pres⁃sure, generating irrational knowledge learning behaviors. Based on the SOR model, it is significant to reveal the intrin⁃sic mechanism of knowledge disconnection and identify the paradoxical relationship between knowledge disconnection and connection, so as to understand knowledge disconnection and intervene dynamically in order to promote the sus⁃tainable development of knowledge subjects and society. [Method/process] This study proposes the concept of knowl⁃edge disconnection based on digital disconnection related research, analyzes the triggering basis, reaction process and implementation strategy of knowledge disconnection, and explores the practical paradoxes of knowledge disconnection and builds a framework for solving the paradoxes on this basis. [Result/conclusion] There are three kinds of behavioral responses, namely filtered, defensive and ambiguous knowledge disconnection, to regulate the mental state and inner perception under multi-source conditioned stimuli. There are value paradoxes, emotional paradoxes and cognitive para⁃doxes in the process of knowledge disconnection practice, which are manifested in the opposing choices of knowledge
disconnection and connection, triggering individuals' paradoxical thinking. This study also constructs a paradoxes reso⁃lution framework that includes three processes: inclusive acceptance, harmonious adaptation, and dynamic synergy, in order to promote the realization of the dynamic balance and virtuous cycle of knowledge disconnection and connection.

[Purpose/significance] This article designs targeted scene governance strategies to respond to complex gov⁃ernance needs. It improves the refinement and scenarization level of the governance of deepfake events public opinion information. It breaks through the limitations of the vertical depth and adaptation accuracy of existing governance strate⁃gies. [Method/process] By integrating risk management theory and scenario theory, this article takes the perception of public opinion information risk in deepfake events as the dependent variable, and forgery technology, public aware⁃ness, media dissemination, degree of harm, scope of impact, response capability, and regulatory norms as independent variables. It has constructed a model of influencing factors and causal driving factors for the perception of public opin⁃ion information risk in the AIGC era of deepfake events. [Result/conclusion] In personal privacy events, none of the variables constitute a necessary condition for high-risk perception. This article has obtained 16 configuration paths for
high-risk perception of public opinion information in deepfake events. It has refined three governance models: risk identification scene, risk assessment scene, and risk response scene, and proposed scene governance strategies for deepfake events public opinion information.

[Purpose/significance] Analyzing the complexity of network public opinion in the context of generative arti⁃ficial intelligence and exploring the risk factors of intelligent generation network public opinion are of great signifi⁃cance for the governance and decision-making of network public opinion risks. [Method/process] This article uses the WSR methodology to construct an indicator system for network public opinion risk elements in three dimensions: physi⁃cal, rational, and human. At the same time, it combines fuzzy DEMATEL and AHPsortⅡ methods to measure and ana⁃lyze network public opinion risk, and uses the Chengdu demolition rumor incident as an empirical case for in-depth ex⁃ploration. [Result/conclusion] Based on empirical data results, this article proposes targeted risk management strate⁃gies for network public opinion from three levels: physical, physical, and human, aiming to provide valuable references for relevant departments and the public to enhance their ability to respond to network public opinion risks.

[Purpose/significance] False short videos integrate multimodal content such as images, text, and audio,with their varying content structures potentially influencing the dissemination of short videos by manipulating user trust. Investigating the impact of multimodal content structure manipulation of false short videos on users′ sharing inten⁃sion aids in identifying false short videos and enhances platforms′ capacity to govern false information. [Method/pro⁃cess] Drawing upon SOR theory, this study constructed a research model in the context of false short videos: "informa⁃tion structure-perceived credibility-user attention-user sharing intention". Data was collected through a mixed-meth⁃ods approach combining questionnaires, eye-tracking experiments, and semi-structured interviews. [Result/conclu⁃sion] Users directed greater attention towards text areas than image areas, whilst exhibiting greater variability in atten⁃
tion distribution within image areas. Based on the structural manipulation of false short videos, this study found that the primary-secondary and parallel relationships exert a significant positive influence on perceived accuracy and consis⁃tency, while causal relationships exert a significant negative influence on perceived accuracy. Perceived accuracy ex⁃erts a significant positive influence on text fixation counts, image fixation counts, and image fixation duration. Text fixa⁃tion counts, image fixation counts, and image fixation duration exert a significant positive influence on users′ sharing in⁃tension.

[Purpose/significance] In the context of data-driven governance, solving the problem of fragmented govern⁃ment data governance policies and achieving overall policy effectiveness improvement have become key breakthroughs in unleashing the value of data elements and building a modern governance system. [Method/process] This article is based on the third-order research paradigm of "dynamic evolution analysis→ collaborative linkage mining → efficien⁃cy improvement path extraction". On the basis of constructing the theoretical framework of government data governance policy efficiency improvement，relying on the GraphRAG framework and integrating the deep semantic reasoning abili⁃ty of the big language model with the multidimensional relationship mapping technology of the knowledge graph, this ar⁃ticle analyzes the evolution process and evolution law of China's government data governance policy. Furthermore, it an⁃alyzes the collaboration of policy subjects and the linkage of policy content elements, explores the collaborative difficul⁃ties of China's government data governance policy, and finally proposes efficiency improvement path suggestions, pro⁃viding a practical direction of "evolution interpretation collaborative optimization efficiency improvement" for optimiz⁃ing government data governance policy. [Result/conclusion] China's government data governance policies have evolved from "block segmentation" to "multi-dimensional collaborative ecology", from "single point breakthrough" to "system empowerment", and from "passive response" to "value consciousness" in cognition; there are collaborative challenges such as "cellular isolation" and "breadth intensity imbalance"; it is necessary to build a cross domain collab⁃orative governance ecosystem, improve the dynamic adaptation mechanism of policy content, and enhance the overall synergy of multidimensional policies in order to improve the overall effectiveness of policies.

[Purpose/significance] This study aims to clarify the concepts and concept relations between data manage⁃ment and data governance, address the current problems of confusion and misapplication of concepts and provide refer⁃ences for building and improving standard systems for data management and data governance in China, facilitating the adoption and appropriate application of international standards and promoting China's active participation in the formu⁃lation of international rules and standards in the digital field. [Method/process] This paper adopts the text content analysis method and analyzes the definitions of data management and data governance from online databases of ISO,IEC, and ITU-T, in accordance with principles of concept system building in ISO 704:2022. From these definitions, the concept objects, concept characteristics, and types of concept relations of data management and data governance are identified, respectively, and core concept relationship diagrams are constructed. The similarities and differences of con⁃
cepts between the two terms are compared across four dimensions: concept objects, concept characteristics, concept re⁃lations and application levels. [Result/conclusion] Data management involves micro and meso levels and encompasses comprehensive activities including data processing, processes management and value realization; data governance cov⁃ers micro, meso, and macro levels, emphasizing quality management, definition of rights and responsibilities, strategic coordination and policy formulation; both share common concerns about process elements and the realization of data value, presenting an associative relation.

[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] Generative AI search engines are becoming the most frequently used and most dis⁃cussed search platforms. However, there remains limited understanding regarding the effectiveness of their system de⁃sign, their ability to support diverse task types, and their capacity to enhance search outcomes for individuals with dif⁃ferent learning styles. This study addresses these gaps to supplement research on search behavior and effectiveness within generative AI search environments in the field of interactive information retrieval. [Method/process] Based on a hierarchical interaction model, we used the New Bing website as our experimental system to investigate the relation⁃ship between system interactions (information resources, page presentation, and technical characteristics) and search effectiveness. We also examined how different task types (factual and exploratory) and learning styles (divergent, con⁃vergent, assimilative, and accommodative) influence user search behavior and search outcomes. [Result/conclusion] Most system interaction metrics correlate positively with search effectiveness at the user level, while cost- benefit search effectiveness relates solely to page layout and typography. Participants demonstrated richer search behaviors and superior search outcomes during factual tasks. Furthermore, significant differences in search behaviors emerged across the four learning styles, primarily reflected in query restructuring frequency and types. Search effectiveness vari⁃ations were equally pronounced, with participants exhibiting divergent learning styles reporting superior experiences in both quality satisfaction and perceived value.

[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] The rapid advancement of large language models（LLMs）has transformed tradition⁃al human-computer interaction. Investigating the characteristics of human-AI conversational interactions among uni⁃versity students in proactive mental health contexts can enhance our understanding of the complexity and diversity of user behaviors in such scenarios. [Method/process] Data were collected using the mobile experience sampling method (mESM). A total of 32 subjects were recruited for a two-week experiment on human-AI conversational interactions.Longitudinal data tracking was implemented through structured diary logs, while participants′ mental health status was assessed before and after the experiment using validated psychological scales. The experimental data were processed and analyzed through a combined approach of open coding and conversation analysis. [Result/conclusion] The study systematically examines the content characteristics of human-AI conversational interactions through three dimensions:user query features, AI response features, and AI interpretability features. It further analyzes behavioral characteristics through conversational structure features and "query-response" behavioral patterns, while also organizing features re⁃lated to mental health dimensions. Additionally, the study proposes three distinct modes of human-AI interaction: diag⁃nostic-consultative interaction, affective-interactive engagement, and cognitive-collaborative coordination.

[Purpose/significance] This study aims to systematically explore the impact of the group effects of push pullfactors on individual digital hoarding behavior. [Method/process] Drawing on push-pull theory and the MOA framework, this study developed a research model encompassing multi-dimensional factors such as emotional attach⁃ment, fear of missing out, intolerance of uncertainty, technology empowerment, digital information characterization, and social impact. By combining Necessary Condition Analysis (NCA) and Fuzzy-Set Qualitative Comparative Analysis (fsQCA), this study identified the core pathways and grouping patterns influencing digital hoarding behaviors. [Result/conclusion] The formation of individual digital hoarding behavior is jointly influenced by push-pull factors, with three
main pathways identified: the pull-dominant opportunistic motivation type, the push-induced motivational stress type,and the push-pull synergistic capacity-supporting type. Accordingly, this study proposes promoting digital environ⁃ment governance through a synergistic approach encompassing technological regulation, cognitive adaptation, and emo⁃tional guidance, thereby achieving the dual objectives of individual psychological adjustment and information environ⁃ment optimization.

[Purpose/significance] Investigating the pathway of user trust in deepfake videos can mitigate blind trust and provide theoretical support for the government and social media platforms to formulate deepfake risk governance strategies. [Method/process] The meta-analysis was used to determine the 12 elements affecting user trust in deep⁃fake videos. The fuzzy ISM and the MICMAC were combined to analyze the correlation pathways and classification rela⁃tionships of user trust in deepfake videos. [Result/conclusion] The pathways to user trust are categorized into literacydriven pathway, platform-environment dependency pathway, and information assessment pathway. Cognitive ability,awareness of deepfakes, and informational cues constitute foundational factors exerting the most profound influence. In⁃ternalization of personal literacy, optimization of the information ecosystem, and reinforcement of external cues are key dimensions for reducing user trust in deepfake videos.

[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.

[Purpose/significance] The unstructured and highly context-dependent nature of emotional information
poses challenges to traditional intelligence processes. Its deep integration with the emotional labor of practitioners
makes the synergy between "value extraction, labor expenditure, and ethical compliance" a core dilemma. Systematical⁃
ly resolving this dilemma is crucial for enhancing intelligence work efficiency. [Method/process] This study construct⁃
ed an "Emotional Information Intelligence Governance Loop" (EIG-Loop) model that incorporates emotional labor regu⁃
lation, based on intelligence cycle theory. Empirical validation was conducted through a mixed-method study involving
30 open-source intelligence analysts and 400 commercial customer service representatives. [Result/conclusion] The
research identified a four-stage information chain fracture mechanism ("collection-processing-decision-feedback") in
traditional processes and confirmed a significant positive correlation between information entropy and emotional labor
intensity. The EIG-Loop model, via its dual-closed-loop design of "information flow-labor regulation", effectively im⁃
proved intelligence conversion efficiency, reduced emotional labor intensity, and enhanced ethical compliance.

[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 paper critically questions and reflects on information forecasting, aiming to deepen the understanding of its characteristics and limitations and promote its sound development. [Method/process]The paper first analyzes the internal logic and theoretical presuppositions of information forecasting, examines its theo⁃retical foundations and questions the rationality of these foundations, and finally proposes the feasible approaches of in⁃formation forecasting. [Result/conclusion] The internal logic of information forecasting is "predicting events based on historical patterns", i.e., inferring the unknown from the known and speculating about the future based on the past and present states of things. This internal logic relies on a fundamental theoretical presupposition: the development of things is governed by social laws, which can be identified through analyzing information about their past and present states, and thus the future of things can be inferred from these laws. However, the theoretical foundations supporting the
internal logic and theoretical presuppositions of information forecasting—namely, the theory of future causality and the principle of continuity—both contain obvious logical flaws and lack internal logical consistency. Therefore, information forecasting should be conducted with foreknowledge as its purpose, indicator monitoring as its method, and information assessment as its core content.

[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] Conflict health information is an important interfering factor in the health informa⁃tion dissemination for the new elderly people. Clarifying the influencing mechanism and role path of conflict health in⁃formation on resistance to persuasion among the new elderly people has important reference value for designing effec⁃tive health information dissemination intervention measures. [Method/process] Based on the person-environment fit theory, this study constructed a multiple serial mediation model to examine the effects of conflict health information on resistance to persuasion. Empirical analysis was conducted through a 2 (conflict health information: conflict vs. nonconflict)×2 (evidence type: non-narrative vs. narrative) between-subjects design, while multi-group analysis and posthoc test was employed to explore the differential effects across different evidence types. [Result/conclusion] Conflict health information indirectly influences resistance to persuasion among the new elderly people; conflict health informa⁃
tion positively affects the information supplies-information needs mismatch and the information demands-information abilities mismatch, and two types of mismatches both result in cognitive dissonance and perceived low efficiency among the new elderly people, which in turn result in resistance to persuasion; compared with non-narrative conflict health in⁃formation, narrative conflict health information result in stronger resistance to persuasion among the new elderly peo⁃ple, and the post-hoc test further showed that narrative conflict health information directly result in active resistance among the new elderly people.

[Purpose/significance] Classifying research topics into their respective disciplines is fundamental for inter⁃disciplinary research, such as measuring interdisciplinary integration and identifying cross-disciplinary themes. Only by determining the disciplinary category of each research topic can we assess whether it represents an interdisciplinary intersection. [Method/process] This study proposes a framework for classifying research topics into disciplines using large language models. Building on base models such as Llama3-8B-Instruct, Qwen2.5-7B-Instruct, and DeepSeek-R1-Distill-Qwen-7B, a two-stage optimization strategy of "domain-adaptive pretraining + supervised fine-tuning" was implemented. A total of 116192 academic papers were used for domain-adaptive pretraining to enhance the model′s semantic understanding of scientific literature. Author keywords were used to represent research topics, and a manual⁃ly annotated dataset of 126919 "research topic-disciplinary label" pairs was employed for supervised fine-tuning to op⁃timize the model′s classification performance. [Result/conclusion] While large language models possess zero-shot dis⁃ciplinary classification capabilities, their precision and F1-scores remain below 50% when relying solely on prompt de⁃sign, which is insufficient for practical applications. In contrast, the proposed framework achieves a precision of 93.61% and an F1-score of 83.09%, significantly improving the accuracy of disciplinary classification for research top⁃ics.