459 Research on the Distribution and Evolution of Interdisciplinarity in the Multidisciplinary Cross-Synthesis Research Field Hot! Cao Jiajun, Wang Yuefen, Chen Shengzhi, Zou Bentao DOI: 10.3772/j.issn.1000-0135.2020.05.001 The purpose of this study is to reveal the core disciplines in a multi-disciplinary field and to analyze the internal relationship and evolution. This study takes artificial intelligence (AI) as its research object, discusses the distribution of related disciplines in this field, and analyzes the relationships, similarities, and evaluations between them to provide data support and decision-making structures for scientific research and policy-making. After pre-processing and analyzing literature data, keywords were used to express the research content of subjects and construct subject vectors through a bag-of-words model. Then, the distribution of AI-related subjects, the similarities and evolution between AI and other subjects, as well as between related subjects are studied from three aspects: Basic statistics, co-occurrence analysis, and similarity analysis. The results indicate that in the field of artificial intelligence, computer science and engineering fields are the most prominent, mathematics is its basis, and its research is gradually spreading to social sciences, biological sciences, and other fields. AI research is developed from single theoretical and technical research to multidisciplinary applications. The diversification of disciplines in this field also promotes the research content diversification of management and law. This shows that the analysis path can reveal the interdisciplinary development trends of subject research to a certain extent. 2020 Vol. 39 (5): 459-468 [Abstract] ( 283 ) HTML (94 KB)  PDF (4415 KB)  ( 824 )

469 The Impact of Interdisciplinarity: Distinct Effects on Usage and Citation Hot! Zhang Lin, Sun Beibei, Wang Xianwen, Huang Ying DOI: 10.3772/j.issn.1000-0135.2020.05.002 With the advantages interdisciplinary research (IDR) offers in promoting comprehensive problem-solving in social development, governments are giving serious attention and extensive support to IDR. However, how to effectively identify and evaluate interdisciplinary outputs still remains an unresolved important question for scientific policy makers. On the basis of traditional citation data, this paper further introduces the emerging usage data (html page views, xml downloads, and pdf downloads) downloaded from PLoS platform to comprehensively evaluate the impact of IDR outputs. Taking scientific papers published in PLoS Computational Biology during 2009-2013 as an example, we reached the following threeconclusions First, there is a positive relationship between interdisciplinarity and impact. For papers with higher interdisciplinarity, the corresponding citation and usage data are significantly higher than those with lower interdisciplinarity. Second, there is an interactive effect between citation data and usage data, and the latter shows a slightly increasing trend which corresponds to the “peak” of citations. Third, interdisciplinarity also has a significant effect on correlations between usage data and citation data. This study attempts to explore the impact of IDR outputs by using both usage data and citation data, thereby opening up a new perspective for IDR evaluations. 2020 Vol. 39 (5): 469-477 [Abstract] ( 184 ) HTML (96 KB)  PDF (2313 KB)  ( 1072 )

478 Interdisciplinary Research in Information Science and Library Science: Progress and Prospects Hot! Gu Xiuli, Huang Ying, Sun Beibei, Zhang Lin DOI: 10.3772/j.issn.1000-0135.2020.05.003 Single discipline fails to conduct in-depth analysis and systematic research on complicated, comprehensive, and integrative problems. Therefore, interdisciplinary research (IDR), which has the potential to solve critical challenges of social development, has gained importance. Based on the bibliographic information of IDR publications, this paper presents a landscape of IDR in the field of Information Science & Library Science (IS&LS), including distribution of countries, leading institutions, core authors, main journals, etc. Furthermore, bibliographic coupling analysis is applied to explore the most-addressed research topics. This systematic analysis of IDR publication provides an overview of current research progress, which is beneficial to promote further exploration of IDR in China. 2020 Vol. 39 (5): 478-491 [Abstract] ( 292 ) HTML (151 KB)  PDF (4023 KB)  ( 822 )

492 Interdisciplinarity Measurement in Publications: From Full Reference Analysis to Sectional Reference Analysis Hot! Zhang Lin, Liu Dongdong, Lyu Qi, Sun Beibei, Huang Ying DOI: 10.3772/j.issn.1000-0135.2020.05.004 Quantitative measurement of interdisciplinarity of relevant research outputs is an important issue in interdisciplinary research (IDR), and is of great significance for detecting and understanding the interdisciplinary phenomenon and the law of discipline development. Diversity measurement based on cited references is a mainstream method of interdisciplinarity measurement. However, most previous studies treated all the references of the paper as a whole while applying interdisciplinarity measures and ignored the distribution of references in separate sections, the importance of different references, and the repetition of some citations. This study first analyzes the cited references in different sections by identifying the citation marking, then calculates the weighted interdisciplinarity according to the importance of different sections by detecting reference distribution in sections. Taking the scientific papers published in PLoS ONE during 2007-2016 as an example, we come to the followingconclusions First, the Introduction section has the highest interdisciplinary diversity on average, followed by Discussion, Method, and Results; second, compared with the previous approach, measuring weighted interdisciplinarity based on the occurrence of cited references in different sections shows relatively low values and a more concentrated distribution. Our proposed approach presents the potential for a deeper and more detailed interdisciplinarity measurement and provides a new perspective on measuring and identifying interdisciplinary outputs. 2020 Vol. 39 (5): 492-499 [Abstract] ( 288 ) HTML (92 KB)  PDF (1676 KB)  ( 826 )

500 An Analysis of the Development Situation and Trends of Cross-Regional Scientific Research Collaboration under the Perspective of Knowledge Exchange Hot! Ye Guanghui, Bi Chongwu DOI: 10.3772/j.issn.1000-0135.2020.05.005 Based on the systematic review of the status quo of cross-regional scientific research collaboration knowledge, this study proposes a research framework for the analysis of the development situation and trend of the cross-regional scientific research collaboration under the perspective of knowledge exchange. Firstly, information seeking and retrieval, health informatics, and user seeking behavior are used as test subjects to construct the knowledge exchange network. Secondly, the cumulative changes of various types of connections between the nodes in the knowledge exchange network are measured to explore the evolution in various topics. Thirdly, the rules of cross-regional knowledge exchange in connection realization, agglomeration distribution, and knowledge dissemination are revealed to explain the evolution power. Finally, the impact of geographic distance, International Network for the Science of Team Science (INSciTS), and knowledge collaboration tool development on the future advancement of knowledge exchange are predicted. According to the results, although the subject knowledge exchange networks are in different stages of evolution, there are relatively close evolution laws: the future impact of geographic distance on cross-regional scientific research collaboration will be weak; the impact area of INSciTS will continue to expand; education, teaching, and training of knowledge collaboration will be the focus of the follow-up attention at the SciTS conference. 2020 Vol. 39 (5): 500-510 [Abstract] ( 244 ) HTML (117 KB)  PDF (916 KB)  ( 593 )

511 Factors Influencing Users Intention to Share Online Health Rumors Based on the MOA Model Hot! Song Xiaokang, Zhao Yuxiang, Song Shijie, Zhu Qinghua DOI: 10.3772/j.issn.1000-0135.2020.05.006 Health rumors are a widespread phenomenon in social media. This paper explores users intention to share online health rumors based on the Motivation-Opportunity-Ability (MOA) model, focusing specifically on the impact of motivation-, opportunity-, and ability-related factors on individuals’ intention to share online health rumors. Three proxy variables were used to measure the MOA framework, namely health consciousness, time cost, and health literacy. A research model was proposed and three hypotheses presented. Eight health rumors were selected in three steps. A total of 252 participants were recruited for online scenario experiments, resulting in 2016 data threads. Five multiple linear regression models were used to test the study s hypotheses. The results showed that participants had higher sharing intention toward cancer rumors than dietary rumors. Men had lower sharing intentions toward online health rumors than women. Older people had higher sharing intentions. Health consciousness and time cost had a positive positive effect on sharing intention, and health literacy had a negative effect. This study contributes to the literature on sharing of health rumors and yields some practical implications for intervention against the spread of health rumors. 2020 Vol. 39 (5): 511-520 [Abstract] ( 188 ) HTML (151 KB)  PDF (746 KB)  ( 1786 )

521 Research on Domain Knowledge Alignment Based on Deep Learning: Knowledge Network Perspective Hot! Yu Chuanming, Li Haonan, An Lu DOI: 10.3772/j.issn.1000-0135.2020.05.007 With the rapid development of big data, knowledge networks have become highly diverse and complex among different languages, domains, and modalities. How to align and integrate heterogeneous knowledge networks under multi-source context is becoming a considerable challenge. Based on the deep representation learning of knowledge networks, this paper proposes a knowledge network alignment (KNA) model, which consists of three modules—the knowledge network construction module, the cross-lingual network representation module, and the statistical machine learning module. To verify the validity of the model, we conducted an empirical study on Chinese and English bilingual knowledge networks and projected heterogeneous knowledge networks into the same space. Based on this process, the statistical machine learning model was designed by using known alignment links between knowledge nodes among different networks, and unknown alignment links were predicted by the model. The KNA model obtained a Precision@1 value (0.7731) in the cross-lingual word co-occurrence network alignment task, which is higher than that of the baseline method (0.6806), which verifies the validity of the KNA model in cross-lingual knowledge network alignment. The research results are of great significance for improving the accuracy of knowledge node alignment among different knowledge networks and for promoting the integration of heterogeneous knowledge networks under multi-source context. 2020 Vol. 39 (5): 521-533 [Abstract] ( 257 ) HTML (149 KB)  PDF (1008 KB)  ( 802 )

534 From Free to Fee: Exploring Askers Switch Behavior on Online Q&A Platforms from the Perspective of Cognitive Lock-in Hot! Zhao Yuxiang, Liu Zhouying, Zhu Qinghua DOI: 10.3772/j.issn.1000-0135.2020.05.008 The wave of sharing economy promotes the innovation of the knowledge sharing model of the online Q&A platforms, deriving the payment-based mode based on the free mode. The number of users who have gradually increased their participation in the payment-based knowledge Q&A platforms is increasing. Based on the push-pull-mooring model and the status quo bias theory, this study explores the askers’ switch behavior from free- to payment-based knowledge Q&A platforms from the perspective of cognitive lock-in. The results of this study indicate that the askers’ switch intention has a significant positive impact on the switch behavior. In terms of mooring factors, financial cost and cognitive lock-in have significant negative impacts on the askers’ switch intention, whereas the subjective norm has a significant positive impact on the askers’ switch intention. Uncertainty cost, free mentality, and habit positively increase the askers’ cognitive lock-in. In terms of pushing factors, low satisfaction causes the askers to switch from free- to payment-based knowledge Q&A platforms, and has a mediating effect on the relationship between the askers’ cognitive lock-in and switch intention. In terms of pulling factors, perceived relative advantage and financial benefit have significant positive impacts on the askers’ switch intention, and the former construct has no significant mediating effect on the relationship between cognitive lock-in and switch intention. This research enriches the theoretical basis of the askers’ switch behavior from free- to payment-based knowledge Q&A platforms, and provides corresponding suggestions on the management and design of the payment-based knowledge Q&A platforms. 2020 Vol. 39 (5): 534-546 [Abstract] ( 310 ) HTML (183 KB)  PDF (1089 KB)  ( 1046 )

547 Progress of Foreign Cognitive Load Theory Application Research Hot! Zha Xianjin, Huang Chengsong, Yan Yalan, Guo Jia DOI: 10.3772/j.issn.1000-0135.2020.05.009 Cognition refers to the process and capacity of an individual s knowledge acquisition and problem-solving, which is also the capability of an individual to process information. The cognitive load theory suggests that an individual s cognition is a type of resource consumption. Individuals need to process cognition and thus consume cognitive resources during the process of knowledge acquisition and problem solving. The main impacting factors of cognitive load include individuals prior experience, intrinsic nature of learning materials, and the approach toward organizing and presenting learning materials. The cognitive load theory has been extensively applied in the fields of Education, Psychology, Computer Science, Business Economics, and Information Science Library Science. The work that proposed the cognitive load theory was treated as the seed paper in our current study. Using the Social Sciences Citation Index/Science Citation Index, a word frequency statistics analysis was conducted based on the keywords provided by the studies citing the seed paper. This study further presented the hot topics of cognitive load theory application research from the citing paper perspective. The method of literature study was then employed, and four subject categories were elicited based on the contents of the citing papers, such as impacting factors and manifestation of cognitive load, impact of cognitive load on learning, impact of cognitive load on behaviors of information system users, and impact of cognitive load on collaborative behaviors. The development trend of cognitive load theory application research was reviewed in terms of these four subject categories. 2020 Vol. 39 (5): 547-556 [Abstract] ( 420 ) HTML (100 KB)  PDF (646 KB)  ( 1759 )

557 Trends of Information Science in the Era of Artificial Intelligence:Rethinking Theories of Ontology, Perception, Methodology, and Service Hot! Cao Wenzhen, Lai Jiyao, Wang Yanfei DOI: 10.3772/j.issn.1000-0135.2020.05.010 With the rapid development and gradual popularization of artificial intelligence (AI) technologies, it is difficult for information science to avoid the trend of integration with AI, irrespective of whether the integration is an active or passive choice. However, the development trend of information science in the era of AI has not been fully discussed, and most of the studies focus on issues concerning the environment of big data and the Next Generation Internet. In this study, these trends are deconstructed into four topics, including ontology, perception, methodology and service, and each topic is interpreted using the dichotomy that includes theory-oriented and practice-oriented. From the overall perspective, it is suggested that the orientation of information science should be placed accurately in the flood of AI, the value of “human” in information science should not be neglected, and the trend of interdisciplinary integration and cross-domain applications should be carefully considered. 2020 Vol. 39 (5): 557-564 [Abstract] ( 458 ) HTML (73 KB)  PDF (744 KB)  ( 1091 )