[Purpose/Significance] By combing the relevant literature at home and abroad in the past ten years, the paper grasps the characteristics and shortcomings of the research on user across social media information behavior, and puts forward the research direction worthy of attention, so as to provide assistance for the development and theoretical breakthrough in this field. [Method/Process] Based on the literature retrieval, the study screened the literature according to the research purpose and quality evaluation. Then, the paper conducted a content analysis to summarize and sort out the literature from the aspects of concept definition, theoretical basis and subject classification. [Result/Conclusion] The research on users across social media information behavior has such conceptual connotations as diversity of user types, heterogeneity of social media, consistency of information clues and collaboration of behavior patterns. The theoretical basis focuses on research background, information needs and information behavior, and the research topics can be classified into information behaviors of different users in different social media and information behaviors of the same user in different social media. At the same time, based on the shortcomings of existing research, future research would further expand the extension of conceptual features, build a proprietary theoretical model and dig deep into its practical value.
[Purpose/Significance] Combining the theory of super network to identify key nodes in cross-social media public opinion dissemination has practical implications for relevant departments to regulate online public opinion and ensure network information security. [Method/Process] The research selected specific online public opinion events and used the same user algorithm across social media to identify information users participating in cross-social media public opinion dissemination. Based on super network theory, various subnets in cross-social media public opinion communication were modelled, and methods such as natural language processing, topic mining and sentiment analysis were integrated to explore key nodes in the process of cross-social media public opinion communication. [Result/Conclusion] Research found that the cross-social media public opinion key node identification method proposed in this study can identify and interpret key nodes in the process of cross-social media public opinion dissemination from different perspectives, thereby better describing the dissemination process and characteristics of cross-social media public opinion, and providing new research methods and ideas for the study of cross-social media public opinion dissemination.
[Purpose/Significance] This paper proposes a recommendation algorithm that integrates cross-platform user preferences and heterogeneous information networks, based on the heterogeneous big data of cross-platform users. It plays a significant role in alleviating the sparsity and cold start problems of personalized recommendation. [Method/Process] Initially, the paper constructed a user core interest social circle based on cross-platform heterogeneous information, captured user information preference features in both the source and target platforms through convolutional neural networks and self-attention mechanisms. Subsequently, it built a heterogeneous information network based on the core interest network and the relationships among recommended items, and it employed a heterogeneous graph attention network model for feature aggregation. Finally, the study integrated the above feature embeddings into an improved matrix factorization model to compute recommendation scores. [Results/Conclusion] The model demonstrates superior performance across four independently constructed cross-platform datasets. This study not only fills the gap in cross-platform, multi-attribute, and fine-grained datasets in the field of recommendation but also enhances the theoretical and methodological system related to recommendation by introducing cross-platform features.
[Purpose/Significance] Mining and visualizing the global disruptive technology, such as the emerging and hot technical topics and their evolution differences implied in the global blockchain patent literature can provide for practitioners in the field, science and technology policy makers, management departments and science and technology research and development personnel. [Method/Process] Based on the patent literature in the global blockchain field, different time slices were divided in a timely order. LDA topic model, Word2vec word vector model, and BERT language model were comprehensively utilized to build the technical topic mining model in the blockchain field. At the same time, four-dimensional indicators, including topic popularity, topic population, topic technicality and topic novelty were constructed to identify the hot technology topics and emerging technology topics in the field of blockchain. These were then combined with the topic evolution model to carry out evolutionary analysis on the emerging hot topics. [Result/Conclusion] The research finds that the LDA2Vec-BERT topic recognition and evolution model could identify emerging and hot technical topics in the field based on the massive patent literature in the field of blockchain, and visually and clearly displays the evolution trend and characteristics of the topic of blockchain technology, and finds the development trend of blockchain technology from architecture research to application research. By comparing the model results, it can be found that the empirical results are reasonable, and the accuracy rate, recall rate and F1 value of the model are higher than other recognition models, which proves that the method has the good effect on the recognition of disruptive technology topics.
[Purpose/Significance] Using interpretability technology and storytelling methods to study cyberbullying detection can help identify bullying content, participate in online public opinion governance, and purify the online ecology. [Method/Process] The study analyzed the selection basis of LIME interpretability algorithm and text interpretability theory in detail and proposed the"Diamond"application process of LIME algorithm in cyberbullying detection model, further constructed a storytelling presentation framework of"data layer-model layer-explanation layer-narrative layer", and finally verified the effectiveness of the framework through experiments. [Result/Conclusion] The interpretability technology for cyberbullying detection model can improve the application value and the model credibility, balance the relationship between model accuracy and interpretability, and the storytelling presentation method based on data analysis and interpretation results can provide a credible, reliable and understandable basis for network information platform data governance.
[Purpose/Significance] Joint extraction of entities and relations is a crucial component in the adverse drug reactions monitoring and knowledge organization.To address the issues of error propagation, entity redundancy and interaction deficiency in traditional pipeline extraction methods, and to improve the extraction effect of overlapping ternary groups of adverse drug reactions, the paper proposes a joint extraction model of adverse drug reactions entities and relations based on heterogeneous graph attention network MF-HGAT. [Method/Process] Firstly, the paper conducted knowledge transfered from external medical corpus resources through pre-training with BERT to achieve the fusion of multiple semantic features.Secondly, the paper introduced relations information as prior knowledge for heterogeneous graph nodes to avoid extracting semantically irrelevant entities.Then, the paper enhanced the representations of characters and relations nodes by iteratively fusing messages with a hierarchical graph attention network through message passing.Finally, the paper extracted drug adverse reactions entities and relations after updating the node representations. [Result/Conclusion] Experiments on self-constructed adverse drug reactions datasets reveal that the joint extraction F1 value of MF-HGAT, which incorporates relations information and external medical and health domain knowledge, reaches 92.75%, which is an improvement of 5.29% over the mainstream model CasRel.The results demonstrate that the MF-HGAT model further enriches entity-relations semantic information by fusing character and relations node semantics through heterogeneous graph attention network, which is of great significance to the knowledge discovery of adverse drug reactions.
[Purpose/Significance] Academic information seeking is usually a long-term, repeated and continuous process, which is easily to encounter interference and obstruction leading to frustration.This paper explores the frustration coping behaviors of college students in academic information seeking to improve the coping effect. [Method/Process] Based on frustration coping theory, semi-structured interview and content analysis method were used to analyze the types of frustration coping behaviors, coping effects and related influencing factors.Then a frustration coping model for academic information seeking was constructed for college students. [Result/Conclusion] The results show that the behaviors of college students coping with information seeking frustration mainly include problem-centered and emotion-centered.The former includes problem management coping and problem evaluation coping, while the latter includes avoidant and non-avoidant emotional coping.Factors of user, task and environment could affect the coping behaviors and effects.Finally, further research problems are pointed out and relevant countermeasures are put forward.
[Purpose/Significance] The process of communication and persuasion in online communities influences and shapes individuals' attitudes and perceptions, but how persuasion works in online communities and promotes attitude change remains to be solved. [Method/Process] This paper selected the Change My View (CMV)sub-community on Reddit as the research site, and adopted a qualitative text analysis method to explore the mode of persuasion communication behavior and attitude change of online community users. [Result/Conclusion] This study summarizes different argumentation structures and interaction patterns, and finds three approaches to attitude change.The results of this study are of great value to enrich the theoretical models of online persuasion and explore effective modes of persuasion communication and interaction.
[Purpose/Significance] The public instinctively search for information to alleviate anxiety caused by various threats.However, unconfirmed and exaggerated information disseminated on the Internet can lead to information overload, psychological resistance, and cyber-hypochondria, resulting in information avoidance behavior.Therefore, it become crucial to understand the mechanism of the shift from information-seeking to information-avoidance behavior when studying user behavior during public health emergencies. [Method/Process] The study reviewed the current status of research on users' information-seeking and information-avoidance behaviors, formulated the corresponding hypotheses, constructed the corresponding theoretical models based on the SOR theory, and empirical studies were conducted using questionnaires and structural equation modeling. [Results/Conclusion] This study utilizes the SOR model to uncover the mechanisms that drive the shift from information-seeking to information-avoidance behavior in public health emergencies.The results indicate that when users perceive a threat and seek information, they may encounter information overload, psychological resistance, and cyber-hypochondria, leading to information avoidance behavior.These insights contribute to a better understanding of user information behavior during public health emergencies and offer a practical reference for information governance.
[Purpose/Significance] Investigating into practice progress of authorized public data operation, aiming to identifying optimized strategies, which contributes to theoretical and methodological system of data elements. [Method/Process] By analyzing the public information of relevant provinces and cities, the current situation of practice were sorted out from three aspects: operation positioning, action elements including action subject, data object, operation mode, operation results and policy and platform protection. [Results/Conclusion] Based on the practical analysis of all aspects of public data authorization operation, the optimization prospect was further proposed: to achieve the balance of multi-dimensional construction and accurate positioning of public data authorization operation, expand and deepen the action elements of public data authorization operation in the process of innovation and exploration, strengthen the allocation of elements of public data authorization operation guarantee, and promote the construction of a national integrated collaborative system.
[Purpose/Significance] The construction of digital health industry data governance system is the basis for promoting digital health industry data governance activities, improving the government's digital precision industry governance and improving the digital capability of enterprises. [Methods/Process] Firstly, the paper analyzed the data status of the digital health industry and the necessity of data governance, and drew on the PDCA cycle theory to put forward the data governance process.Secondly, based on the data governance specification and the five-element integration theory, the paper designed the logical framework of the data governance system for the digital health industry from the five dimensions of governance subject, governance object, governance activity, governance tool and governance target.Finally, on the basis of the logical framework, the paper constructed the technical architecture of the digital health industry data governance system based on the data center. [Result/Conclusion] The logical framework and technical framework of data governance system for digital health industry constructed in the paper can enhance the value of theoretical system research and technical practice of industrial data governance, provide reference for the data governance practice of digital health industry, promote the digital governance and sustainable development of digital health industry, and improve the data governance capabilities of governments and enterprises.
[Purpose/Signficance] Under the background that cross-border data flows boost the development of globalization, this paper explores the path of collaborative governance of China's cross-border data flows, and provides reference for improving the efficiency of collaborative governance of China's cross-border data flows. [Methods/Process] This paper introduced the SFIC collaborative governance theoretical model and modifies it for applicability.By analyzing the practical difficulties of cross-border data flow collaborative governance in China, it proposed solutions based on the modified SFIC model. [Results/Conclusions] A five-dimensional relief path framework of"Starting Conditions-institutional design-Facilitative Leadership-collaborative process-evaluation and feedback"is constructed, and a number of specific measures are put forward, such as cultivating collaborative governance culture, stimulating the enthusiasm of collaborative subjects for governance, and building a unified collaborative governance platform.It aims to promote the long-term, sustainable and healthy development of collaborative governance of China's cross-border data flow.
[Purpose/Significance] DEA analysis has been widely used in the evaluation of academic journals, but its systematic error has not been paid attention to.Therefore, an in-depth study of the root causes of this issue and its solution strategies can more accurately reveal the true quality of academic journals. [Method/Process] In this paper, the systematic error theory was introduced into DEA analysis.On the basis of theoretical analysis, the generation mechanism and influence of systematic error of DEA method in academic journal evaluation were deeply explored, and effective methods to prevent and reduce systematic error were proposed. [Result/Conclusion] It is found that the evaluation of DEA academic journals in China is still in the exploratory stage.The causes of systematic errors of DEA methods in the evaluation of academic journals include systematic errors in the determination of evaluation purpose, systematic errors in the selection of input-output variables, systematic errors in the applicability of evaluation methods, model setting errors and systematic errors in the publication of results.The selection of evaluation index and method is the main cause of systematic error of DEA method in academic journal evaluation.The paper also proposes a solution to prevent and eliminate systematic errors in DEA efficiency analysis of academic journals: carefully selecrts input and output indicators of efficiency evaluation of academic journals, ensures that the number of evaluation objects reaches a certain scale, and selects evaluation methods according to the evaluation purpose.It is not appropriate to abuse complex DEA models, and the expert empowerment method should be adopted to set weights.The efficiency analysis of science and technology evaluation also has the problem of systematic error, which needs to be dealt with according to local conditions.For academic evaluation with the attribute of public goods, DEA method should be appropriately adopted.
[Purpose/Significance] The evaluation of knowledge exchange efficiency and exploration of influencing factors of information resources management journals in China is of great significance to promoting the high-quality development of information resources management journals in China. [Method/Process] Based on the paper and citation data of 20 CSSCI source journals in the field of information resources management from 2013 to 2021, this paper used the global super- efficiency SBM model to calculate knowledge exchange efficiency, and used kernel density estimation and Markov chain to analyze the evolution trend of knowledge exchange efficiency.The knowledge exchange efficiency level of information resources management journals in China was captured from both static and dynamic perspectives.The dynamic QCA method based on panel data was used to explore the configuration changes in the factors influencing the efficiency of knowledge exchange in academic journals, taking into account both time effects and individual effects. [Results/Conclusions] It is found that there is a large gap in knowledge exchange efficiency among information resources management journals in China, with typical non-equilibrium characteristics.Although the level of knowledge exchange efficiency has gradually improved, the absolute difference is widened, indicating the presence of the"Matthew Effect".The factors influencing knowledge exchange efficiency exhibit significant time effects and individual effects.The improvement of knowledge exchange efficiency is the result of multiple factors working together.Considering the heterogeneity of journals, each journal can achieve factor linkage based on its own situation, further promoting the improvement of knowledge exchange efficiency of information resources management journals in China.
