[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] 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] The construction of a knowledge organization system (KOS) relies on collective ne⁃gotiation among expert groups to obtain a conceptual system for specific domain knowledge organization needs, but it is difficult to cover the needs of multi domain resource organizations. With the development of diversified knowledge and the demand for solving complex problems, people have put forward requirements for the construction of diversified knowledge organization systems. Explores the theoretical basis of knowledge organization system construction from a di⁃versified perspective, can provide a reference theoretical basis for the construction and sustainable development of di⁃versified knowledge organization systems. [Method/process] This article mainly analyzes the reasons for the diversity of KOS from the two key elements that dominate the construction of KOS: the object (resource environment) and the sub⁃ject (cognitive subject); furthermore, the theoretical framework and fundamental for constructing a diversified knowl⁃edge organization system was explored, providing a reference theoretical basis for the construction and organic integra⁃tion of diversified knowledge organization systems. [Result/conclusion] Complex resource environments and personal⁃ized cognitive subject thinking are the main contributing factors to diversified KOS. Based on multiple theories such as literary warrant, ethics of care, epistemology, and cultural hospitality, the theoretical thinking on the construction and organic integration of diversified knowledge organization systems has been clarified, in order to enable KOS to adapt to the diverse changes and development needs of the social environment and promote its sustainable development.

[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] 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] 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 integration of the digital economy and generative AI has exacerbated the trust crisis in data circulation, making the construction of a trusted data space a key path to break through the predicament.Systematically analyze libraries, archives, and data intermediaries institutions′ differences and complementarities are of significant importance for optimizing disciplinary practices and building a collaborative governance system. [Meth⁃od/process] This paper centers on the construction of trusted data space within the realm of information resource man⁃agement. It compares the differences among libraries, archival institutions, and data intermediaries institutionsin as⁃pects including data trustworthiness, data governance maturity, data circulation efficiency, and social credibility. [Re⁃sult/conclusion] Three types of subject institutions have varying degrees of differences in the standards for building a trusted data space. The governance strategies proposed include leveraging disciplinary advantages to make up for tech⁃nical deficiencies, establishing a division of labor and collaboration mechanism centered on scenarios, and constructing a governance framework that is incentive-compatible and dynamically coordinated. These strategies provide a theoreti⁃cal framework and path selection for the construction of a trusted data space in the AIGC era.

[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.

[Purpose/significance] Addressing critical needs in digital economy development, this study establishes a theoretical framework for data trading platforms and investigates their construction modes, providing theoretical and practical guidance for platform operations. [Method/process] Applying the LDA model to policy texts for topic model⁃ing, this study constructed the framework of data trading platforms, followed by comparative case studies of representa⁃tive government-led and enterprise-led platforms through systematic platform investigations. [Result/conclusion]This study established a five-in-one theoretical framework for data trading platforms. Government-led platforms pay more attention to platform ecological construction and compliance requirements, which adopt the constructing mode of "policy orientation+ecological synergy+normative guidance". Enterprise-led platforms focus on flexible service and value realization, which adopt the construction mode of "flexibility and autonomy+precision marketing+diversified ser⁃vice". The two types of platform construction basically conform to the policy orientation. The government-led platforms should strengthen technological innovation, promote data supply and enhance market-oriented operation ability. Enter⁃prise-led platforms should deepen cooperation, improve compliance mechanism and strengthen whole process manage⁃ment.

[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] As generative artificial intelligence (GAI) grows more dependent on training data,super platforms have used their structural advantages to dominate model training, creating a "platform-model" symbiot⁃ic relationship. However, existing research has paid little attention to the operational mechanisms of platform control and the associated governance dilemmas within the training data phase. [Method/process] Using normative analysis and institutional comparison, this paper dissects the unique power structure and risk types inherent to the "platformmodel" symbiosis and reveals their risk formation and governance pathways. [Result/conclusion] The study finds that training data risks under this symbiotic relationship are endogenous and amplified, challenging traditional regulatory models. To address this, it proposes a multi- stakeholder collaborative governance system featuring: a principled,tiered, government-led source documentation framework to manage privacy leakage and data crossover risks; a verifi⁃able platform self-assessment system to dismantle governance barriers; and superplatform-led construction of trusted data spaces to mitigate data circulation risks.

[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.