[Purpose/Significance] Evaluating the effectiveness of the implementation of FAIR principles for government open data platform helps the platform to standardize identifiers, unify metadata standards, etc., and enhance the discovery, sharing and reuse of government data. [Method/Process] A network research method was used to build an assessment index system of FAIR principles for government open data platforms by drawing on an international representative assessment framework and combining research, and eight sample platforms at home and abroad were selected as cases to carry out assessment and analysis in four dimensions, such as discoverability and accessibility, to compare the status and differences of FAIR principles implementation. [Results/Conclusions] The corresponding improvement strategies are proposed in terms of permanent identifier scheme, metadata standard specification and data usage license, etc. to improve the data management specifications of domestic government open data platforms and provide theoretical guidance and practical reference for the construction and utilization of subsequent related platforms.
[Purpose/Significance] The application of FAIR principle in scientific and technological journals helps to enhance the discoverability, interaction, sharing, and reuse of journal paper supporting data. Evaluating the implementation of FAIR is beneficial for its promotion and implementation, with the aim of providing useful references for supporting data sharing and reuse in Chinese journal papers. [Method/Process] On the basis of foreign FAIR principle evaluation models, the paper comprehensively considered the advantages of each model and the design characteristics of indicators, and combined the characteristics of supporting data related to papers in"Data Analysis and Knowledge Discovery"to build a FAIR principle index evaluation system. Based on this system, the paper analyzed the survey results from four dimensions. Finally, the paper proposed rationalization suggestions and optimization strategies for the application of FAIR to the support data of Chinese journal papers. [Result/Conclusion] The application of FAIR principles in supporting data for journal papers still needs further improvement, and researchers' awareness of data sharing and understanding of FAIR principles are far from sufficient. The paper proposes to promote FAIR principles and implementation from both macro and micro levels to promote more open and reusable data.
[Purpose/Significance] In order to reveal the new form of associated publishing of data papers and journal papers, the research on the open sharing of current data journals and the association between data papers and journal papers is helpful to promote the development of open sharing of scientific data, promote the efficient circulation of scientific data, and release the data value of scientific data in multiple dimensions. [Method/Process] Based on the FAIR principle, from the perspectives of metadata elements and literature services, the mutual association model between Data papers and journal papers was constructed from the perspective of data flow, the association process between data papers and journal papers was analyzed, and the data papers of representative data journals Data in Brief were selected as examples to carry out model validation and practice reference. [Result/Conclusion] This paper studies"Open sharing"based on "Accessible" and "Findable". The "Association" is studied based on "Interoperable" and "Reusable". Through the construction of theoretical model and case verification, the correlation between data papers and journal papers is clarified and the feasibility and rationality of the theoretical model is verified.
[Purpose/Significance] In order to serve the country's major strategic needs and seize the historical opportunity of discipline renaming, grasping the overall development context of first-level disciplines which becomes an important link in building China's independent information resource management knowledge system. [Method/process] The data source was the core journals of the discipline included in CNKI. First, the study used K-Means clustering algorithm and the classification method integrating TF-IDF algorithm and LDA Topic model to calculate the optimal number of topics and identified the content of research topics; Then, it conducted an in-depth analysis from three aspects: the intensity of the topic before and after the renaming, the novelty of the topic, and the development and changes of the research topic; Finally, this study proposed an analytical method for predicting the future development direction of research topics based on the intersection of topic words. [Result/Conclusion] The renaming of the first-level discipline of information resource management would stimulate the emergence and development of new topics. The expansion of new topics in research content, scope, and other aspects in turn enriches the connotation and extension of this discipline, which has certain reference value for the future development of the discipline's standardization and innovation.
[Purpose/Significance] A comparative analysis of the subject discourse power status of China and the United States in the field of Information Science and Library Science will help to speed up the construction of a disciplinary system of Philosophy and Social Sciences with Chinese characteristics, and find deficiencies to improve discourse power. [Method/Process] The article first used the LDA topic mining model to realize topic extraction technology and combined the hot topic identification indicators to clarify the distribution trend of hot topics in China and the United States in the field of Information Science and Library Science in the past decade; On this basis, the evaluation indicators of subject discourse status were preliminarily screened, then indicators were selected through expert scoring and the weight of indicators were determined by the same method, forming a quantitative formula for the evaluation of subject discourse power, and then explored the overall status of the discourse power in China and the United States from the perspective of the distribution of hot topics.By sorting out the characteristics and changing trends of hot topics, this paper analyzed the evolution of the subject discourse power of China and the United States in this field from aspects of breadth and depth; Finally, this paper combined the theory of discourse analysis to condense the subject discourse power of China and its promotion path. [Result/Conclusion] China's outstanding academic leadership in the field of international Information Science and Library Science has achieved high-quality dissemination of disciplinary discourse, and its great academic influence has helped our country to accumulate discourse power, but there is a long way to go in regulating the contextual control of disciplinary discourse.
[Purpose/Significance] Through analyzing the public's perception of individual, industry, and government aspects in the metaverse, this paper provides targeted recommendations for individual, enterprise and government to better construct the metaverse ecosystem. [Method/Process] Referring to the Triple Helix Model, the paper constructed an analysis framework for public perception of the metaverse based on the individual-industry-government dimensions.Based on Zhihu Q&A data, the paper used improved LDA and BERT algorithms to analyze public perceptions of individual, industry, and government aspects in the metaverse, including public's concerns, topic hotness, sentiment and their evolution. [Result/Conclusion] The public's perception in the dimensions of individual, industry and government are different and related.From the perspective of evolution, the public's concerns are gradually refined and deepened, and their attitudes are significantly influenced by experience and public opinion, but are gradually becoming more rational.Through mining the hotspots, potential points, and existing issues in different dimensions, the paper provides targeted recommendations for different participants.
[Purpose/Significance] Based on objective data, the study forms a set of automatic screening methods to quickly identify the quality of patent results and provides decision support to promote the transformation of patent results. [Methodology/Process] Firstly, the study constructed a high-quality patent results screening index system with combining the formal features such as the number of inventors and the number of IPC numbers of patent results with the semantic vector matching degree features and the quality annotation results of patent results; Secondly, taking the field of"advanced manufacturing and automation"as an example, the study retrieved the invention patents in this field on the Patent Star platform as the source of patent text data, and took the demand of Hubei Province as an example, and took its relevant industrial development plan(macro)and market technology demand(micro)as the source of demand text data.; then, processed the patented text and the demanded text by using word separation, de-stopping, text vectorization and other steps, and organized to form a training set and a test set; finally, called eight machine learning classification algorithm model for training and evaluation, and tested the algorithm with the best training effect for application to verify the feasibility of the screening method. [Results/Conclusion] The results show that the random forest algorithm model has the best overall performance among the selected eight types of algorithm models, and is used as the kernel classification algorithm in the screening method of high-quality patent results.In addition, the screening method proposed in this paper has a strong feasibility for the quality identification of patent results and can combine the specific patent needs of different provinces(municipalities)to quickly screen large quantities of patent results, which face to a certain extent, effectively reduce the consumption of human, material and financial resources costs.
[Purpose/Significance] Based on the perspective of value co-creation, the division of physicians into groups could effectively portray the value co-creation level of knowledge services of different physician groups, and provide support for the segmentation management of physician groups and the sustainable development of online medical communities. [Method/Process] In this paper, firstly, based on the theory of value co-creation, the study constructed the physician group feature system through theoretical combing.Further, 21 946 physician samples of website haodf.com were obtained through web crawler.Then, used the clustering algorithm to divide the samples into groups.Finally, analyzed the feature differences of different physician groups. [Result/Conclusion] The results show that physicians could be classified into five groups: high-value co-creative physicians, general value co-creative physicians, low-value co-creative physicians, high value co-creation potential physicians and value co-destructive physicians, and physicians of different value co-creative types have significant differences in value co-creation level, knowledge service performance, individual characteristics and disease category distribution.The results of this paper extend the theoretical system of knowledge service and information user research in online communities and help to promote value co-creation between physicians and patients and sustainable community development.
[Purpose/Significance] In order to meet the urgent needs of researchers for efficient querying of fine-grained semantic information within scientific and technological literature, previous studies have proposed a multidimensional semantic indexing system for scientific and technological literature, however, the common inverted indexes based on HashMap lead to inefficient querying.This paper aims to improve the semantic query performance by establishing hybrid inverted indexes for different dimensional semantic features. [Method/Process] This paper explored the inverted index construction methods suitable for different semantic dimensions with Treap, B+ tree and other data structures, and combined them to form a variety of hybrid inverted index construction methods suitable for multidimensional semantic organization of scientific and technological literature, and analyzed and verified the query performance of the different types of inverted index construction methods under the conditions of Top-k query and Boolean query through comparative experiments. [Result/Conclusion] The experimental results show that among the eight hybrid inverted index construction methods formed by the combination, C3 (HHHB)shown in Table 2 is proved to have the highest efficiency under the condition of Top-k query, while C4 (TTTB)is proved to be the most efficient under the condition of Boolean query.The method in this paper can effectively solve the query efficiency problem caused by a single index structure.
[Purpose/Significance] This research aims to explore the impact of platform characteristics on User-Generated Content(UGC)sharing behavior across social media platforms, revealing new features of user behavior in cross-social media scenarios. [Method/Process] Drawing from the Stimulus-Organism-Response(SOR)theoretical framework, the study constructed a mediating model to understand the influence of platform characteristics on cross-social media UGC sharing behavior.The paper integrated data from application stores, Baidu Index, and social media across time and platform dimensions, created a multi-source heterogeneous panel dataset as our sample.The hypotheses were tested to use a fixed-effects model. [Results/Conclusion] The platform's reputation and the iterative advancements in user service technology positively influence cross-social media UGC sharing through perceived usefulness.Conversely, the platform's attention level negatively affects sharing behavior via perceived usefulness.Moreover, the platform's reputation and iterative advancements in user service technology have a negative moderating effect on the relationship between perceived usefulness and cross-social media UGC sharing.This study focuses on the sharing behavior of UGC across social media platforms, offering insights for UGC creators to adopt cross-platform content dissemination strategies and providing guidance for social media platforms to maintain a balanced ecosystem.
[Purpose/Significance] The aim of this study is to explore the influencing factors and intrinsic mechanisms of online health community users' algorithm avoidance behavior, and to provide countermeasures and suggestions for optimizing algorithm service and regulation in online health communities. [Method/process] The study used the Cognition-Affect-Conation pattern as the theoretical framework to build a theoretical model of the factors influencing the algorithm avoidance behavior of online health community users.The study also analyzed and validated the influencing factors through a three-stage SEM-ANN-NCA data analysis method. [Result/Conclusion] At the cognitive level of user engagement in online health communities, privacy concern, perceived intrusiveness, perceived threat, and system function overload positively influence users' algorithm anxiety, and all are necessary conditions for algorithm anxiety.The importance of the four cognitive factors for algorithmic anxiety is ranked from highest to lowest in terms of perceived intrusion, privacy concern, system function overload, and perceived threat.At the affective level, algorithm anxiety positively influences algorithm avoidance behavior and it is a necessary condition for algorithm avoidance behavior.
[Purpose/Significance] Accurate prediction of the evolution of public opinion is significance in resolving public opinion crises. [Method/Process] To solve the shortcomings of the existing HK model for predicting the evolution of social users' opinions, the following improvement were given: Firstly, the initial opinion values of users were obtained from historical blog posts with similarity to the current event above a threshold.Secondly, the user confidence threshold was revised by calculating the affinity using the interaction behavior between users and the following relationship.Then, the global influence of users, the interaction between users and the proximity of opinions were combined to calculate the comprehensive trust degree between users, highlighting the differences in the weight of opinion influence between users.Finally, the improved HK model was used to predict the evolution of social users' opinion. [Result/Conclusion] The experimental results show that the improved HK model for opinion evolution prediction has lower MSE and MAE values and can provide effective predictions for opinion development.
[Purpose/Significance] This paper proposes and constructs a group view point extraction model, which divides the opinions of online public opinion groups while extracting key keywords to demonstrate the essence of group viewpoints, providing reference for public opinion guidance. [Method/Process] Based on the LDA topic model and TextRank keyword model, this paper constructed a group opinion extraction model and captured the online public opinion data with pig+futures as the key words as the experimental sample.After calculating the optimal number of group opinions, the study divided group opinions and extracted core keywords to reflect the semantic connotation of opinions. [Result/Conclusion] The group viewpoint extraction model based on LDA and TextRank can effectively classify group viewpoints based on semantic connotations and extract core keywords.After extraction and sorting, the keywords can clearly reflect the core semantic connotations of each group viewpoint, which is helpful for serving financial and public opinion regulatory agencies to carry out reasonable public opinion monitoring and guidance work based on the spot market situation, semantic connotations of group viewpoints, and the stage of peak quantity.
[Purpose/Significance] In the context of the strategy of cultural strengthening, China's academic discourse is now at an important stage of development.The evaluation of academic discourse can help promote China's academics to meet the challenges of the new era, promote the internationalization and localization of academic discourse with China's characteristics, and clarify the general direction and specific measures for the development of Chinese academic discourse in the future. [Method/Process] This paper adopted literature and network research, comparative study, scientometrics and other methods to systematically sort out the basic theory and practice of academic discourse at home and abroad, based on the development practice of academic discourse at home and abroad, based on the perspective of academic subjects, with reference to the relevant academic achievement evaluation index system, and the empirical study of Chinese social science disciplines, on the basis of which, the study further considered and analyzed the evaluation system and the realization path to enhance China's academic discourse. [Result/Conclusion] The core elements of the evaluation of China's academic discourse are mainly composed of four parts: academic leadership, academic competitiveness, academic innovation and academic communication.Through the empirical analysis, it is found that most of Chinese social science disciplines are still relatively low in international status, and finally, it is proposed that the international influence and status of China's academic discourse can be comprehensively improved from these four aspects.
[Purpose/Significance] Academic papers serve as crucial evidence and indicators of scholars' research level and academic contributions. Constructing a scientific academic paper evaluation model holds significant guiding implications for talent assessment, research fund allocation, awards, promotions, recruitments, and more. [Methods/Process] This study selected papers published in the "Information Science and Library Science" discipline category in the Web of Science database in the year 2010 as the research subjects. Firstly, a model feature space was constructed based on various correlated characteristics of the papers. Then, a supervised learning algorithm widely used in predictive tasks in machine learning—the BP neural network—was employed to train the model. Ten-fold cross-validation was conducted to ensure model stability. Finally, by calculating the model's adjusted coefficient of determination(Radjusted2)and root mean square error(RMSE), the optimal model was selected. [Results/Conclusion] The optimal BP neural network model constructed in this study achieved an adjusted coefficient of determination(Radjusted2)of 0.91 and an RMSE of approximately 19.8, demonstrating good evaluation performance.
