[Purpose/Significance] In today's age of data & intelligence, data & intelligence technology is the key driving force of the Construction of Safety & Security Intelligence Capability(CSSIC), so it is significant to study CSSIC empowered by data & intelligence. [Method/Process] Based on analyzing the transformation of safety & security intelligence capability in the age of data & intelligence, this paper revealed the mechanism of CSSIC empowered by data & intelligence and put forward the path of CSSIC empowered by data & intelligence. [Result/Conclusion] Through CSSIC empowered by data & intelligence, it can realize the platform capability integration, flat organizational structure distribution, forward-looking global analysis, and systematic collaborative operation of safety & security intelligence capability.CSSIC empowered by data & intelligence should be oriented towards the whole process of safety & security intelligence demand determination, safety & security intelligence acquisition, safety & security intelligence analysis, safety & security intelligence application and feedback.

[Purpose/Significance] Personalized recommendations have played a significant role in the commercial evolution of diverse content platforms, with users' understanding growing deeper.While existing research extensively evaluates recommendation algorithms from a user-centric standpoint, there remains a gap in assessing these algorithms from an ethical perspective.Therefore, this study focuses on the ethical dimension, explores the rationality of personalized recommendations on content platforms and its impact on users' intention to continue using these platforms, aiming to fill this research gap. [Method/Process] This study, addressing both user interests and public welfare, employed normative analysis to introduce the concept of'Rationality of Personalized Recommendations on Content Platforms", comprising Technical Rationality, Content Rationality, and Ethical Rationality dimensions.Drawing from prior studies, the paper devised operational indicators and constructed a scale to validate a theoretical model regarding the impact of recommendation rationality on users' intention to continue usage.Empirical research methodologies were utilized to investigate causal mechanisms. [Result/Conclusion] Findings indicate that the newly developed scale for evaluating the rationality of personalized recommendations on content platforms demonstrates high reliability and validity.Additionally, it reveals a significant positive influence on users' intention to continue using the platforms.

[Purpose/Significance] This paper explores the external inducements and internal motivations of users' information avoidance behavior on broadcast social network platforms, reveals the rules of users' information avoidance behavior, and puts forward guiding suggestions for users to improve information utilization efficiency and enterprises to improve information service quality. [Method/Process] Based on S-O-R model, the factors and mechanism of information avoidance behavior of users of broadcast social network platform were analyzed by qualitative research method, and a model of information avoidance behavior of users of broadcast social network platform was constructed based on their behavioral motivation and action path. [Result/Conclusion] Information overload, information interest and weak connectivity were taken as external stimulus.It leads to the user's perceived cost and perceived value motivation, and then drives the user's information avoidance behavior.

[Purpose/Significance] As a passive behavior, users' intermittent discontinuance may make it difficult for generative AI to retain users and achieve a competitive advantage.Thus, it is necessary to examine the formation mechanism of user intermittent discontinuance and find the significant factors. [Method/Process] From a C-A-C perspective, this research examined the enablers and inhibitors of generative AI users' intermittent discontinuance.Enablers include privacy concern, information hallucination and cognitive dissonance, while inhibitors include intelligence, anthropomorphism, personalization and emotional commitment.SEM and fsQCA were adopted to conduct data analysis. [Results/Conclusions] Privacy concern and information hallucination affect cognitive dissonance, which further leads to intermittent discontinuation.Intelligence, anthropomorphism, and personalization affect emotional commitment, which prevents intermittent discontinuance.The results imply that generative AI needs to mitigate users' privacy concern and reduce information hallucination in order to lower cognitive dissonance.On the other hand, generative AI needs to enhance the intelligence, anthropomorphism, and personalization in order to increase users' emotional commitment and prevent their intermittent discontinuance.

[Purpose/Significance] Based on the characteristics of information itself and individual, this study explores the influencing factors of the adoption intention of individual health rumor refuting information, and clarifies the influencing path of each influencing factor, so as to provide theoretical basis and enlightening suggestions for improving the rumor refuting effect of health rumor refuting information. [Method/Process] Based on the extended Information Adoption Model and Aristotle's rhetoric theory, this study constructed a model of influencing factors of health rumor refuting information adoption intention, and further verified the moderating effects of individual cognitive conflict and knowledge self-confidence on different influence paths. [Result/Conclusion] The results show that logos and ethos have a significant positive effect on information usefulness.Pathos, ethos and logos had a bias effect.Pathos, ethos and logos have a negative synergistic effect on information usefulness.On the conscious path, cognitive conflict positively moderates the effect of ethos and pathos on information usefulness, and knowledge self-confidence negatively moderates the effect of logos on information usefulness.In the unconscious path, knowledge self-confidence positively moderates the influence of information usefulness on information adoption intention and negatively moderates the influence of herding factors on information adoption.

[Purpose/Significance] The value Creation of data elements has become a core issue in the development of China's digital economy in the context of Digital China.A systematic examination of it, revealing the current development characteristics and patterns of each region, and improving the overall logical path can help promote the release of data value. [Method/Process] Based on the theory of information ecology, the study established an ecological view of data elements, constructed a model of factors affecting the value creation of data elements to explored the linkage effects of multiple driving factors at the data layer, technology layer, subject layer, and environmental layer.Using 27 provinces(autonomous regions)as analysis samples, the study identified the development characteristics of different regions, proposed optimization ideas and ultimately clarified the systematic logic path of data element value creation. [Result/Conclusion] There are three characteristic patterns in the current practice of data element value creation in various regions: mature leading, fully catching up, and government driven.Mature leading regions, with their existing advantages, can quickly respond to national data development strategies, comprehensively promote data element value creation, and thus generate leading digital economy effects; Although there are shortcomings in data resource endowments in fully catching up regions, they rely on good technological capabilities, industrial foundations and strong government support, gradually showing a trend of post value development of data elements; The government driven regions rely more on the promotion of government departments, data industries, and policy systems, and this top-down development path has become the main model for data value creation in regions with relatively backward development levels of the big data industry.The study proposes a systematic development path for a data element value creation driven by ontology, subject, technology, and environmental factors.Each region should accurately position its own development characteristics and optimize the continuous flow and value release of data elements in the system in a targeted manner.

[Purpose/Significance] Based on the five-layer model of user experience, the paper analyzes the influencing factors of the public data open platform of user experience, in order to help the platform improve its construction and realize the upgrade of user experience. [Methods/Processes] Based on the five-layer model of user experience, the paper analyzed the factors, designed hypotheses and constructed models, collected information by using questionnaires, and tested paths, verified hypotheses, analyzed the necessity of the data by using the SEM model and the NCA method. [Result/Conclusions] Among the five-layer of user experience, content requirements, interaction design, interface design and visual performance all have sufficiently significant influence on user experience; in terms of necessity, they are slightly different, and according to the bottleneck level analysis, the degree of necessity is from high to low, which is the content requirements, visual performance, interface design and user requirements, respectively.

[Purpose/Significance] This article analyzes the element value of public data resources in China, which helps to promote stable and far-reaching development of the digital economy, achieve stable and harmonious digital society, and enhance the modernization level of the national governance system and governance capacity. [Method/Process] Firstly, the study used panel data from 31 provinces(municipality, region)in China(excluding Hong Kong, Macao, and Taiwan)over a period of 20 years as samples. Secondly, it used the PSM-DID method for propensity score matching and double difference. Finally, the study investigated the causal effects of public data resource openness and regional economic development in China through robustness and placebo tests. [Result/Conclusion] Open public data resources in China has significantly promoted the economic development level of pilot areas. To enhance the value of public data resources, it is necessary to form a resource opening mechanism led by the government and coordinated by multiple entities, an asset development mechanism based on data rights confirmation and scenario application as the core, and a factor market mechanism with the market as the main body and government department supervision and maintenance.

[Purpose/Significance] The main academic evaluation method is the multi-attribute evaluation of the index system, and the evaluation method undoubtedly has an important impact on the evaluation result, so it is of great significance to introduce the idea of quality diagnosis into the academic evaluation. [Method/Process] Based on the classification of multi-attribute academic evaluation methods, this paper analyzed the influence mechanism of evaluation methods on academic evaluation results, established the quality diagnosis framework of academic evaluation methods, and put forward the quality diagnosis evaluation system of academic evaluation methods. [Results/Conclusion] The results show that it is necessary to attach importance to evaluation method diagnosis and incorporate it into the daily work of evaluation. It is necessary to establish an evaluation method diagnosis path combining subjective and objective methods. It is suggested that the combination evaluation method should be cancelled and the compound evaluation method should be used cautiously in academic evaluation, and pay attention to strengthen the standardization of subjective evaluation methods. It is beneficial to improve the quality of evaluation to advance the diagnostic thought of evaluation method into the evaluation process.

[Purpose/Significance] This study investigates the interdisciplinary patterns of outstanding scientists from the perspective of academic careers, aims to provide certain insights for individual interdisciplinary pattern selection and the development of disciplinary integration. [Method/Process] Taking the winners of Derek de Solla Price Memorial Medal, the highest award in the field of scientometrics as examples, this study examined their interdisciplinary patterns from two dimensions-publication of papers and sources of knowledge. It delineated their academic careers into four stages-incipient, growth, deepening and maturation-to analyze the evolutionary features of their interdisciplinary patterns. [Result/Conclusion] This study identifies four types of scientists with distinct interdisciplinary patterns-amphibious scientists, indigenous scientists, immigrant scientists and resident scientists. Scientists from these categories exhibit differentiated interdisciplinary performance across various stages of their academic careers. They have made outstanding contributions to the development of scientometrics at different periods.

[Purpose/Significance] Based on the self-narration text, this paper explores the evolution law of users' motivation to participate in network health hot events and deeply understands the influence of event heat and event development on users' motivation to participate, which is of great significance for relevant departments to strengthen public opinion guidance and event management. [Method/Process] Firstly, researchers identified situational events and participating users, and collected autobiographical texts in the form of "self-narration+problem guidance". Then, researchers marked the event stage and motivation type in the autobiographical text, and extracted the factors that may affect the motivation change. Finally, combined with the self-narration text annotation, researchers analyzed the evolution law of motivation type and motivation intensity. [Result/Conclusion] The motivations of user participation are health task-based motivation, follow-up display motivation, mixed motivation based on health task-based motivation, and mixed motivation based on follow-up display motivation. The findings suggest that event generation, event controversy, and opinion convergence have an important impact on the change of motivation types. The analysis indicates that the heat of the event has significant impact on the intensity of the follow-up display motivation and has less impact on the intensity of the health task motivation.

[Purpose/Significance] This study aims to analyze the formation mechanisms of information cocoons and explore pathways to break through them, providing users with scientific strategies for overcoming these barriers. [Method/Process] The paper began with the fundamental scenarios of information acquisition and incrementally introduced influencing factors, constructing an integrated model of "formation-evolution-breakthrough" for information cocoons. Simulations were conducted on three scenarios that users might encounter: information acquisition rate, cocoon-breaking choices, and attitudes towards breakthroughs, yielding simulation conclusions with guiding value for breaking through information cocoons and providing mathematical support for users to formulate scientific strategies. [Result/Conclusion] The research finds that moderately slowing down the rate of information acquisition helps users break through information cocoons. Actively embracing external interventions is beneficial for broadening users' channels of information perception, thereby assisting them in overcoming information cocoons; cognitive factors are key to achieving breakthroughs, and users should actively enhance their cognitive levels. Personalized recommendation algorithms have a dual impact on information cocoons, potentially exacerbating the polarization of information. Therefore, users should adopt a proactive stance, using recommendation algorithms as tools to verify their cognitive reflections, which in turn can aid them in breaking through information cocoons.

[Purpose/Significance] In the context of cultural big data strategy, promoting the comprehensive and in-depth development of historical newspaper data resources can help build and improve the cultural big data system. [Method/Process] Taking the "People's Daily (1946—1949)" war event as an example, this article constructed a historical newspaper data resource war event ontology, automatically extracted war events and their constituent elements, and combined the constructed ontology model and extracted data to draw a historical newspaper data resource war event knowledge graph and complete semantic queries. [Result/Conclusion] Realizing the formalization, standardization, and fine-grained representation and organization of the structural hierarchy, characteristic connotations, and connectivity relationships of war event knowledge units in historical newspaper data resources, laying the foundation for gradually building a domain knowledge base and providing refined knowledge services, providing new perspectives and ideas for the research and development of historical newspaper, and assisting in the protection and inheritance of Chinese culture.

[Purpose/Significance] Historical classics are an important component of Chinese civilization, a cultural treasure with great inheritance and research value, and have received widespread attention from scholars. However, the digital development of historical classics is constrained by conditions such as multi-source heterogeneity and complex content. Therefore, scholars have introduced a thick data analysis model. [Method/Process] This study started from the perspective of thick data and used evidence-based theory as theoretical support to propose a framework for constructing a fusion graph of Qing Dynasty criminal cases driven by thick data. The implementation mechanism and path were briefly explained. Finally, taking Qing dynasty ministry of revenue criminal case data as an example, the effectiveness and feasibility of the proposed fusion graph of Qing dynasty criminal cases were verified. [Result/Conclusion] This study is based on the perspective of thick data analysis and uses evidence-based theory as a link to explore the implementation of the fusion graph of Qing Dynasty criminal cases driven by thick data. It aims to increase the readability and credibility of Qing Dynasty criminal cases, optimize the development mode of Qing Dynasty criminal cases, explore the fusion path of different types of visualization graphs, and assist in the knowledge construction and service of historical classics.