期刊：
Information Processing & Management,2023年60(4):103350 ISSN：0306-4573
通讯作者：
Li, DTC;Shi, FB
作者机构：
[Zheng, Chao; Wang, Jian; Li, Duantengchuan; Wang, Jingxiong; Li, Bing] Wuhan Univ, Sch Comp Sci, Wuhan, Peoples R China.;[Zhang, Qi] Cent China Normal Univ, Sch Informat Management, Wuhan, Peoples R China.;[Shi, Fobo; Shi, FB] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Peoples R China.;[Cai, Yuefeng] ZTE Corp, Wuhan 430223, Peoples R China.;[Wang, Xiaoguang; Zhang, Zhen] Wuhan Univ, Sch Informat Management, Wuhan, Peoples R China.
通讯机构：
[Li, DTC ] W;[Shi, FB ] C;Wuhan Univ, Sch Comp Sci, Wuhan, Peoples R China.;Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Peoples R China.
摘要：
Knowledge graphs are sizeable graph-structured knowledge with both abstract and concrete concepts in the form of entities and relations. Recently, convolutional neural networks have achieved outstanding results for more expressive representations of knowledge graphs. However, existing deep learning-based models exploit semantic information from single-level feature interaction, potentially limiting expressiveness. We propose a knowledge graph embedding model with an attention-based high-low level features interaction convolutional network called ConvHLE to alleviate this issue. This model effectively harvests richer semantic information and generates more expressive representations. Concretely, the multilayer convolutional neural network is utilized to fuse high-low level features. Then, features in fused feature maps interact with other informative neighbors through the criss-cross attention mechanism, which expands the receptive fields and boosts the quality of interactions. Finally, a plausibility score function is proposed for the evaluation of our model. The performance of ConvHLE is experimentally investigated on six benchmark datasets with individual characteristics. Extensive experimental results prove that ConvHLE learns more expressive and discriminative feature representations and has outperformed other state-of-the-art baselines over most metrics when addressing link prediction tasks. Comparing MRR and Hits@1 on FB15K-237, our model outperforms the baseline ConvE by 13.5% and 16.0%, respectively.
期刊：
JOURNAL OF DOCUMENTATION,2023年79(2):442-467 ISSN：0022-0418
作者机构：
[Li, Xuguang; Zhang, Yao] Nankai Univ, Dept Informat Resources Management, Tianjin, Peoples R China.;[Li, Xuguang] Shandong Univ Technol, Inst Informat Management, Zibo, Peoples R China.;[Luo, Xiaoying] Lib Shenzhen MSU BIT Univ, Shenzhen, Peoples R China.;[Cox, Andrew] Univ Sheffield, Informat Sch, Sheffield, S Yorkshire, England.;[Lu, Yingying] Cent China Normal Univ, Sch Informat Management, Wuhan, Peoples R China.
关键词：
University students;Mental health information;Online information resources;Mental well-being;Information need
摘要：
Purpose
This research aims to explore the nature of Chinese students' mental health information needs and to identify the online resources they use to meet those needs.
Design/methodology/approach
Data was collected from three Chinese research-oriented universities using semi-structured interviews and a survey. Twenty-five university students with varied backgrounds were selected for semi-structured interviews to explore the triggers and nature of their needs. Then, printed and online questionnaires were distributed to undergraduate and postgraduate students and 541 valid responses were processed for descriptive statistical analysis and variance analysis.
Findings
The following findings were incurred. First, the triggers of university students' mental health information needs mainly are mental health being in the news, personal interest in gaining mental health knowledge, mental health issues, required formal learning and preparation for mental health counselling. Second, eleven types of information are used, with an emphasis on employment pressure, study stress and self-understanding. Third, mental health information needs differ with mental health status and some social-demographic factors (including gender, urban or rural origin and educational stage). Fourth, information needs can be characterized as dynamic; complex and diverse but concentrated on a few types; ambiguous and hard for participants to define; private; stigmatized; self-dependent and substitutable. Fifth, Internet sources used to meet such needs are mainly search engines, Question and Answer platforms, public social media platforms. Finally, a model of mental health information needs was built based on the above findings to map the whole process from what triggers a need, to the content and characteristics of information need, and online resources used to meet those needs.
Practical implications
The paper provides suggestions for university mental health services in developing more tailored knowledge contents via effective delivery methods to meet diverse needs of student groups.
Originality/value
This research is novel in using empirical data to build a holistic model that captures the context and the nature of mental health information needs of university students.
期刊：
Journal of Information Science,2023年 ISSN：0165-5515
通讯作者：
Wu, D;Liu, XZ
作者机构：
[Dong, Jing] Cent China Normal Univ, Sch Informat Management, Wuhan, Peoples R China.;[Kang, Yangyang; Sun, Changlong] Alibaba Grp, Shanghai, Peoples R China.;[Liu, Jiawei; Wu, Dan] Wuhan Univ, Sch Informat Management, Wuhan, Peoples R China.;[Fan, Shu] Sichuan Univ, Sch Publ Adm, Sichuan, Peoples R China.;[Jin, Huchong] Indiana Univ, Luddy Sch Informat Comp & Engn, Bloomington, IN USA.
通讯机构：
[Wu, D ] W;[Liu, XZ ] I;Wuhan Univ, Sch Informat Management, 299 Bayi Rd, Wuhan 430072, Hubei, Peoples R China.;Indiana Univ Bloomington, 107 S Indiana Ave, Bloomington, IN 47405 USA.
关键词：
Active learning;annotation cost;crowdsourcing;ground truth labels;human annotations;human-centred design
摘要：
Active learning in machine learning is an effective approach to reducing the cost of human efforts for generating labels. The iterative process of active learning involves a human annotation step, during which crowdsourcing could be leveraged. It is essential for organisations adopting the active learning method to obtain a high model performance. This study aims to identify effective crowdsourcing interaction designs to promote the quality of human annotations and therefore the natural language processing (NLP)-based machine learning model performance. Specifically, the study experimented with four human-centred design techniques: highlight, guidelines, validation and text amount. Based on different combinations of the four design elements, the study developed 15 different annotation interfaces and recruited crowd workers to annotate texts with these interfaces. Annotated data under different designs were used separately to iteratively train a machine learning model. The results show that the design techniques of highlight and guideline play an essential role in improving the quality of human labels and therefore the performance of active learning models, while the impact of validation and text amount on model performance can be either positive in some cases or negative in other cases. The 'simple' designs (i.e. D1, D2, D7 and D14) with a few design techniques contribute to the top performance of models. The results provide practical implications to inspire the design of a crowdsourcing labelling system used for active learning.
作者机构：
[Hu, Fengying] Hubei Univ, Business Sch, Wuhan 430062, Peoples R China.;[Zhou, Zhenglong] Cent China Normal Univ, Sch Informat Management, Wuhan 430079, Peoples R China.
通讯机构：
[Zhenglong Zhou] S;School of Information Management, Central China Normal University, Wuhan, China
通讯机构：
[Ma, X ] Z;Zhongnan Univ Econ & Law, Sch Informat & Safety Engn, Wuhan, Peoples R China.
关键词：
Paper recommendation;Time-aware;Dynamic preferences;Long/short-term research interests
摘要：
With the number of scientific papers growing exponentially, recommending relevant papers for researchers has become an important and attractive research area. Existing paper recommendation methods pay more attention to the textual similarity or the citation relationships between papers. However, they generally ignore the researcher's dynamic research interests which affect the recommendation performance to a large extent. Additionally, cold start is also a serious problem in existing paper recommender systems since many researchers may have few publications, which makes the recommender systems fail to learn their preferences. In order to solve these issues, in this paper, we propose a Time-Aware Paper Recommendation (TAPRec) model, which learns researchers' dynamic preferences by encoding the long-term and short-term research interests from their historical publications. The Self-Attention method is utilized to aggregate researchers' consistent long-term research interests, while the short-term research focuses are implemented with Temporal Convolutional Networks (TCN). In addition, for researchers with few academic achievements, we combine their co-authors' dynamic preferences to solve the cold-start problem. Experiments with the DBLP dataset indicate that the proposed time-aware model performs better in the recommendation accuracy compared to the state-of-the-arts methods.
摘要：
Keeping track of translational research is essential to evaluating the performance of programs on translational medicine. Despite several indicators in previous studies, a consensus measure is still needed to represent the translational features of biomedical research at the article level. In this study, we first trained semantic representations of biomedical entities and documents (i.e., bio-entity2vec and bio-doc2vec) based on over 30 million PubMed articles. With these vectors, we then developed a new measure called Translational Progression (TP) for tracking biomedical articles along the translational continuum. We validated the effectiveness of TP from two perspectives (Clinical trial phase identification and ACH classification), which showed excellent consistency between TP and other indicators. Meanwhile, TP has several advantages. First, it can track the degree of translation of biomedical research dynamically and in real-time. Second, it is straightforward to interpret and operationalize. Third, it doesn't require labor-intensive MeSH labeling and it is suitable for big scholarly data as well as papers that are not indexed in PubMed. In addition, we examined the translational progressions of biomedical research from three dimensions (including overall distribution, time, and research topic), which revealed three significant findings. The proposed measure in this study could be used by policymakers to monitor biomedical research with high translational potential in real-time and make better decisions. It can also be adopted and improved for other domains, such as physics or computer science, to assess the application value of scientific discoveries.
期刊：
Information Processing & Management,2023年60(2):103220 ISSN：0306-4573
通讯作者：
Quan Lu<&wdkj&>Hui Liu
作者机构：
[Zhang, Lu; Chen, Jing] Cent China Normal Univ, Sch Informat Management, Wuhan 430079, Peoples R China.;[Lu, Quan] Wuhan Technol & Business Univ, Inst Digital Commerce, Wuhan 430065, Peoples R China.;[Lu, Quan] Wuhan Univ, Ctr Studies Informat Resources, Wuhan 430072, Peoples R China.;[Liu, Hui] Chinese Acad Med Sci & Peking Union Med Coll, Inst Med Informat, Beijing 100020, Peoples R China.;[Chen, Shuaipu] Wuhan Univ, Sch Informat Management, Wuhan 430072, Peoples R China.
通讯机构：
[Quan Lu; Hui Liu] I;Institute of Medical Information, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100020, China<&wdkj&>Institute of Digital Commerce, Wuhan Technology and Business University, Wuhan, 430065, China<&wdkj&>Center for Studies of Information Resources, Wuhan University, Wuhan, 430072, China
关键词：
Gaze;Gesture;Health information identification;Misinformation;Predicting information usefulness
作者机构：
[Lu, Quan; Yao, Sumei; Wang, Fan] Wuhan Univ, Ctr Studies Informat Resources, Wuhan, Peoples R China.;[Chen, Jing] Cent China Normal Univ, Sch Informat Management, Wuhan, Peoples R China.;[Lu, Quan] Wuhan Univ, Big Data Inst, Wuhan, Peoples R China.
通讯机构：
[Lu, Q ] W;Wuhan Univ, Ctr Studies Informat Resources, Wuhan, Peoples R China.;Wuhan Univ, Big Data Inst, Wuhan, Peoples R China.
关键词：
Depression;Social media;Posts and replies;Online health community;Systematic review;Text
摘要：
Purpose - Social media texts as a data source in depression research have emerged as a significant convergence between Information Management and Public Health in recent years. This paper aims to sort out the depression-related study conducted on the text on social media, with particular attention to the research theme and methods. Design/methodology/approach - The authors finally selected research articles published in Web of Science, Wiley, ACM Digital Library, EBSCO, IEEE Xplore and JMIR databases, covering 57 articles. Findings - (1) According to the coding results, Depression Prediction and Linguistic Characteristics and Information Behavior are the two most popular themes. The theme of Patient Needs has progressed over the past few years. Still, there is a lesser focus on Stigma and Antidepressants. (2) Researchers prefer quantitative methods such as machine learning and statistical analysis to qualitative ones. (4) According to the analysis of the data collection platforms, more researchers used comprehensive social media sites like Reddit and Facebook than depression-specific communities like Sunforum and Alonelylife. Practical implications - The authors recommend employing machine learning and statistical analysis to explore factors related to Stigmatization and Antidepressants thoroughly. Additionally, conducting mixed-methods studies incorporating data from diverse sources would be valuable. Such approaches would provide insights beneficial to policymakers and pharmaceutical companies seeking a comprehensive understanding of depression. Originality/value - This article signifies a pioneering effort in systematically gathering and examining the themes and methodologies within the intersection of health-related texts on social media and depression.
摘要：
This study examines the evolution of current interests and emerging characteristics in library and information science (LIS) from Chinese iSchools, including an analysis of the LIS landscape, space distribution, citation, emerging characteristics, and collaborations. This study considers a non-parametric approach to outline the structure of the iSchool movement in China, while clustering analysis helped us obtain information about the descriptions generated within unsupervised learning groups. It was found that Chinese iSchools play an intermediary role in the international development of Chinese LIS, which further promotes the dissemination and exchange of knowledge and international cooperation in LIS.
摘要：
Interdisciplinary topic reflects the knowledge exchange and integration between different disciplines. Analyzing its evolutionary path is beneficial for interdisciplinary research in identifying potential cooperative research direction and promoting the cross-integration of different disciplines. However, current studies on the evolution of interdisciplinary topics mainly focus on identifying interdisciplinary topics at the macro level. More analysis of the evolution process of interdisciplinary topics at the micro level is still needed. This paper proposes a framework for interdisciplinary topic identification and evolutionary analysis based on BERTopic to bridge the gap. The framework consists of four steps: (1) Extract the topics from the dataset using the BERTopic model. (2) Filter out the invalid global topics and stage topics based on lexical distribution and further filter out the invalid stage topics based on topic correlation. (3) Identify interdisciplinary topics based on disciplinary diversity and disciplinary cohesion. (4) Analyze the interdisciplinary topic evolution by inspecting the intensity and content in the evolution, and visualize the evolution using Sankey diagrams. Finally, We conduct an empirical study on a dataset collected from the Web of Science (WoS) in Library & Information Science (LIS) to evaluate the validity of the framework. From the dataset, we have identified two distinct types of interdisciplinary topics in LIS. Our findings suggest that the growth points of LIS mainly exist in the interdisciplinary research topics. Additionally, our analysis reveals that more and more interdisciplinary knowledge needs to be integrated to solve more complex problems. Mature interdisciplinary topics mainly formed from the internal core knowledge in LIS stimulated by external disciplinary knowledge, while promising interdisciplinary topics are still at the stage of internalizing and absorbing the knowledge of other disciplines. The dataset, the code for implementing the algorithms, and the complete experiment results will be released on GitHub at:
https://github.com/haihua0913/IITE-BERT
.
作者机构：
[Tang, Xuli] Cent China Normal Univ, Sch Informat Management, Wuhan 430079, Peoples R China.;[Li, Xin] Huazhong Univ Sci & Technol, Tongji Med Coll, Sch Med & Hlth Management, Wuhan 430030, Peoples R China.;[Ma, Feicheng] Wuhan Univ, Sch Informat Management, Wuhan 430074, Peoples R China.
通讯机构：
[Xin Li] S;School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
关键词：
International collaboration;Artificial intelligence;Geographic distance;Economic distance;Cultural distance;Academic distance;Industrial distance;AI
摘要：
International collaboration has become imperative in the field of AI. However, few studies exist concerning how distance factors have affected the international collaboration in AI research. In this study, we investigate this problem by using 1,294,644 AI related collaborative papers harvested from the Microsoft Academic Graph dataset. A framework including 13 indicators to quantify the distance factors between countries from 5 perspectives (i.e., geographic distance, economic distance, cultural distance, academic distance, and industrial distance) is proposed. The relationships were conducted by the methods of descriptive analysis and regression analysis. The results show that international collaboration in the field of AI today is not prevalent (only 15.7%). All the separations in international collaborations have increased over years, except for the cultural distance in masculinity/felinity dimension and the industrial distance. The geographic distance, economic distance and academic distances have shown significantly negative relationships with the degree of international collaborations in the field of AI. The industrial distance has a significant positive relationship with the degree of international collaboration in the field of AI. Also, the results demonstrate that the participation of the United States and China have promoted the international collaboration in the field of AI. This study provides a comprehensive understanding of internationalizing AI research in geographic, economic, cultural, academic, and industrial aspects.
摘要：
Interest in assessing research impacts is increasing due to its importance for informing actions and funding allocation decisions. The level of innovation (also called “innovation degree” in the following article), one of the most essential factors that affect scientific literature’s impact, has also received increasing attention. However, current studies mainly focus on the overall innovation degree of scientific literature at the macro level, while ignoring the innovation degree of a specific knowledge element (KE), such as the method knowledge element (MKE). A macro level view causes difficulties in identifying which part of the scientific literature contains the innovations. To bridge this gap, a more fine-grained evaluation of academic papers is urgent. The fine-grained evaluation method can ensure the quality of a paper before being published and identify useful knowledge content in a paper for academic users. Different KEs can be used to perform the fine-grained evaluation. However, MKEs are usually considered as one of the most essential knowledge elements among all KEs. Therefore, this study proposes a framework to measure the innovation degree of method knowledge elements (MIDMKE) in scientific literature. In this framework, we first extract the MKEs using a rule-based approach and generate a cloud drop for each MKE using the biterm topic model (BTM). The generated cloud drop is then used to create a method knowledge cloud (MKC) for each MKE. Finally, we calculate the innovation score of a MKE based on the similarity between it and other MKEs of its type. Our empirical study on a China National Knowledge Infrastructure (CNKI) academic literature dataset shows the proposed approach can measure the innovation of MKEs in scientific literature effectively. Our proposed method is useful for both reviewers and funding agencies to assess the quality of academic papers. The dataset, the code for implementation the algorithms, and the complete experiment results will be released at:
https://github.com/haihua0913/midmke
.
期刊：
International Journal of Environmental Research and Public Health,2022年19(18):11195- ISSN：1661-7827
通讯作者：
Sui Li<&wdkj&>Jiandong Huang
作者机构：
[Zhang, Heng] Anhui Univ Finance & Econ, Sch Management Sci & Engn, Bengbu 233030, Peoples R China.;[Chang, Qian] Cent China Normal Univ, Sch Informat Management, Wuhan 430079, Peoples R China.;[Li, Sui] Anhui Univ Finance & Econ, Sch Stat & Appl Math, Bengbu 233030, Peoples R China.;[Huang, Jiandong] Guangzhou Univ, Sch Civil Engn, Guangzhou 510006, Peoples R China.;[Huang, Jiandong] China Univ Min & Technol, Sch Mines, Xuzhou 221116, Jiangsu, Peoples R China.
通讯机构：
[Sui Li; Jiandong Huang] A;Authors to whom correspondence should be addressed.<&wdkj&>School of Mines, China University of Mining and Technology, Xuzhou 221116, China<&wdkj&>School of Civil Engineering, Guangzhou University, Guangzhou 510006, China<&wdkj&>Authors to whom correspondence should be addressed.<&wdkj&>School of Statistics and Applied Mathematics, Anhui University of Finance and Economics, Bengbu 233030, China
关键词：
sponge city construction;data envelopment analysis;efficiency evaluation;water ecological environment
摘要：
Sponge city construction (SCC) has improved the quality of the urban water ecological environment, and the policy implementation effect of SCC pilots is particularly remarkable. Based on the data envelopment analysis (DEA) model, this study employed the related index factors such as economy, ecology, infrastructure, and the population of the pilot city as the input, and the macro factors of SCC as the output, to scientifically evaluate the relative efficiency between the SCC pilots in China. Eleven representative SCC pilots were selected for analysis from the perspectives of static and dynamic approaches, and comparisons based on the horizontal analysis of the efficiency of SCC pilots were conducted and some targeted policy suggestions are put forward, which provide a reliable theoretical model and data support for the efficiency evaluation of SCC. This paper can be used as a reference for construction by providing a DEA model for efficiency evaluation methods and thus helps public sector decision makers choose the appropriate construction scale for SCC pilots.
期刊：
Journal of Informetrics,2022年16(4):101333 ISSN：1751-1577
通讯作者：
Xuli Tang
作者机构：
[Li, Xin] Huazhong Univ Sci & Technol, Sch Med & Hlth Management, Tongji Med Coll, Wuhan 430030, Hubei, Peoples R China.;[Tang, Xuli] Cent China Normal Univ, Sch Informat Management, Wuhan 430079, Hubei, Peoples R China.;[Cheng, Qikai] Wuhan Univ, Sch Informat Management, Wuhan 430074, Hubei, Peoples R China.
通讯机构：
[Xuli Tang] S;School of Information Management, Central China Normal University, Wuhan 430079, Hubei, China
关键词：
Clinical citation count prediction;Multilayer perceptron neural network;Reference dimension;Biomedical paper
摘要：
The number of clinical citations received from clinical guidelines or clinical trials has been con-sidered as one of the most appropriate indicators for quantifying the clinical impact of biomedical papers. Therefore, the early prediction of clinical citation count of biomedical papers is critical to scientific activities in biomedicine, such as research evaluation, resource allocation, and clinical translation. In this study, we designed a four-layer multilayer perceptron neural network (MPNN) model to predict the clinical citation count of biomedical papers in the future by using 9,822,620 biomedical papers published from 1985 to 2005. We extracted ninety-one paper features from three dimensions as the input of the model, including twenty-one features in the paper dimen-sion, thirty-five in the reference dimension, and thirty-five in the citing paper dimension. In each dimension, the features can be classified into three categories, i.e., the citation-related features, the clinical translation-related features, and the topic-related features. Besides, in the paper di-mension, we also considered the features that have previously been demonstrated to be related to the citation counts of research papers. The results showed that the proposed MPNN model outper-formed the other five baseline models, and the features in the reference dimension were the most important. In all the three dimensions, the citation-related and topic-related features were more important than the clinical translation-related features for the prediction. It also turned out that the features helpful in predicting the citation count of papers are not important for predicting the clinical citation count of biomedical papers. Furthermore, we explored the MPNN model based on different categories of biomedical papers. The results showed that the clinical translation-related features were more important for the prediction of clinical citation count of basic papers rather than those papers closer to clinical science. This study provided a novel dimension (i.e., the ref-erence dimension) for the research community and could be applied to other related research tasks, such as the research assessment for translational programs. In addition, the findings in this study could be useful for biomedical authors (especially for those in basic science) to get more attention from clinical research.
作者机构：
[Li, Xuguang] Nankai Univ, Dept Informat Resource Management, Business Sch, 94 Weijin Rd, Tianjin 300071, Peoples R China.;[Wang, Zefeng] Shenzhen Energy Grp Co Ltd, Informat Management Dept, 2060 Jintian Rd, Shenzhen 518000, Peoples R China.;[Lu, Yingying] Cent China Normal Univ, Sch Informat Management, 152 Luoyu Rd, Wuhan 430079, Hubei, Peoples R China.
通讯机构：
[Zefeng Wang] I;[Yingying Lu] S;School of Information Management, Central China Normal University, 152 Luoyu Road 430079, Wuhan, Hubei province, China<&wdkj&>Information Management Department, Shenzhen Energy Group Co., Ltd., 2060 Jintian Road 518000, Futian District, Shenzhen, China
摘要：
Environmental factors significantly affect the dynamic strategic alignment of information systems (IS) in coping with a rapidly changing environment, especially for state-owned enterprises (SOEs) in emerging economies. By examining the case study of a medium-sized Chinese state-owned real estate company, this research aims to investigate what environmental factors affect SOEs’ strategic alignment. The thematic analysis method was used to analyse semi-structured interviews with staffs at all levels of the organisation and relevant strategy documents. The research develops a holistic model to illustrate the influencing factors from SOEs’ internal (i.e., organisational structure, culture and resources) and external environments (i.e., politics, economy, technology and national culture) while highlighting the overall influences from the nature of SOEs. It identifies that organisational resources, whose easy access is enabled by SOEs’ state-owned character, significantly reduce the need for strategic alignment to gain competitive advantage in the short term. In addition, SOEs’ inherent attributes of organisational structure and culture impede the social and structural integration between business and IT. Managerial implications are suggested with a particular emphasis on internal factors, such as developing IS/IT from organisational resources and cultivating the perception of the importance of IS. The paper also points to the importance of implementing government authorities’ policies and instructions about IS strategy.
