A case study on knowledge communication based on friends-list links in the science blogging community at Sciencenet.cn

Sciencenet.cn is the leading online portal serving the Chinese scientific community. This paper intends to analyze the interdisciplinary and intradisciplinary knowledge communication patterns based on friends-list links in the blog community at Sciencenet.cn by using hyperlink analysis and social network analysis. The major findings are: 1) More bloggers have an academic background in management science and life science; 2) there are some core actors in co-inlink network and co-outlink network, who take the lead in engaging with knowledge exchange activities and produce a great influence on interdisciplinary communication; 3) interactive relationships commonly exist between a blogger and those on his/her friends list, and the most linked-to blogs usually play a key role in generating interactive communication; 4) management science has the highest co-inlink count with life science or information science and it has the highest co-outlink count with life science or mathematical and physical science; 5) management science and life science have the greatest impact on information science and the interdisciplinary knowledge communication will also produce relatively significant influence on the development of information science discipline. It is our hope that this research can serve as a reference source for the future studies of academic virtual communities, and the development of mechanisms for facilitating increased engagement in knowledge exchange activities in academic virtual communities.

How is pandemic perceived in China?A case study of COVID-19

To help the government better understand and manage public sentiments,and help the public establish the values of rational participation in online discussions related to COVID-19,it is necessary to explore the themes and emotions of different subjects discussing the pandemic on social media platforms.The study takes a comprehensive view by combining social media and scholarly outputs data.In particular,WeChat articles are investigated to reveal the public concern and public sentiment towards COVID-19,and WeChat mentions to scholarly papers are identified to show the interaction between the public and researchers.Text analysis is conducted to construct co-occurrence networks and reveal the distribution of themes.VOSviewer is applied to network visualization.Statistical and comparative analysis showed that discussion about COVID-19 keeps hot on WeChat.WeChat offical accounts from the information industry dominate,suggesting a free and flexible discussing environment.Topics on WeChat overlap with that of scholarly papers but have a much broader scope.WeChat mentions to scholarly papers has bridged the public with the research and has a high coverage of 61.7%.Public sentiment in WeChat is positive,demonstrating good confidence in defeating the pandemic.These findings are helpful in understanding the social attitude towards and comprehensive perception of COVID-19 in China.

How to classify bibliometrics indicators?A thorough investigation of objective classification and its application

Classification of bibliometric indicators is a fundamental issue in information science.Traditionally,the classification is based on subjective classification.This article presents an empirical study on the mathematics journals listed in JCR 2019 by using objective classification methods including cluster analysis,factor analysis,and principal component analysis to classify bibliometric indicators.Different classification results are compared and further interpreted,major finding are:the classification results of objective classification methods share similarities;objective classification helps better comprehend bibliometric indicators;objective classification should be used in combination with subjective classification;cluster analysis performs better in classifying bibliometric indicators than factor analysis and principal component analysis;not all the results of objective classification are meaningful;cluster of indicators has sufficient influence on subsequent evaluation and regression analysis.This study provides a new paradigm for journal classification and indicator analysis.

Altmetrics mentioned in WeChat articles:Evolution,topic,context and comparison with scholarly publications

[Purpose/Significance]The purpose is to explore the use of We Chat official accounts articles(referred to as We Chat articles)as a type of Chinese altmetrics data source,and reveal the attention and discussion surrounding altmetrics in the social media environment,as well as discover the similarities and dissimilarities compared to that of scholarly publications.[Methodology/Procedure]Using We Chat articles that are relevant to altmetrics as the research object,statistical analysis,quantitative analysis,and text mining were used to explore the pattern of attention and discussion of altmetrics in We Chat articles.Meanwhile,scholarly publications of altmetrics were collected for scientometric analysis.The similarities and dissimilarities as regards the degree of attention,topic distribution and developing trend were compared between these two datasets.[Results/Conclusions](1)Number of We Chat articles that mention altmetrics is increasing rapidly,although there is a time lag between the first We Chat article and the first scholarly publication of altmetrics.(2)Types of We Chat official accounts that pay attention to altmetrics are very diversified and go beyond the academia.(3)We Chat articles relevant to altmetrics mainly focus on 4 topics,i.e.the introduction of the latest publications of altmetrics,information of relevant scholarly activities and scholarly meetings,informetrics research and scientific evaluation that involve altmetrics,and introduction of altmetrics monographs.(4)Four major types of context where altmetrics is mentioned by We Chat articles are identified.They are to introduce the concept,theory,knowledge system,and technical methods of altmetrics,to discuss the data sources and research objects of altmetrics,to discuss the construction and application of altmetrics indicators,and to discuss the meaning and value of altmetrics.(5)In contrast,scholarly publications of altmetrics are more centered on systematic research,including the theories of altmetrics,the construction of altmetrics indicators,the application of altmetrics indicators,impact evaluation,and the relationship between altmetrics and traditional informetrics.These results are useful for further developing the Chinese altmetrics data source and understanding the relationship between altmetrics and bibliometrics.

Automatically identifying the motivations of Facebook mention to scholarly outputs based on Light GBM

[Purpose/Significance]The article investigated the automatic identification of the motivation of Facebook mention to scholarly outputs based on Light GBM algorithm,in order to achieve more in-depth usage of Facebook mention on a large scale.[Methodology/Procedure]Based on three types of contextual data,including mentioned scholarly outputs,Facebook users who post scholarly outputs,and text of Facebook posts to scholarly outputs,promising relevant features were extracted,and machine learning algorithms were used to automatically identify the motivations.[Results/Conclusions](1)Features significantly correlated to the motivation of Facebook mention are identified in all three types of contextual data.In particular,relevant features are the altmetric attention score,the number of collaborative countries,the number of followers,the number of likes,the identities of Facebook users who post scholarly outputs and the number of comments on Facebook posts;(2)The prediction precision of the Light GBM classification model for motivation of Facebook mention was 0.31.In comparison,the classification precision without the text features of Facebook posts was 0.35,which was higher than the overall feature combination.The classification precision with only the post text features was 0.27.After combining the length and language of posts,the precision was improved to 0.30;(3)The classification precision of Facebook motivation has a positive correlation with users’activity.After combining all features,the classification precision of the first quartile users in terms of productivity reached 1,the classification precision of the second quartile was 0.36,and for the third quartile,the classification precision was 0.32.In conclusion,considering the high complexity of automatic classification of motivation of Facebook mentions,the study has achieved relatively high classification precision and could provide reference for future studies.

Some basic problems in advancing the development of altmetrics

With systematic survey,induction and analysis of altmetrics literature,the study aimed to answer the following three questions:1)What does altmetrics study?What is it alternative for?2)What is the relationship between altmetrics indicators and traditional citation-based indicators?And why?3)Are altmetrics indicators valid?Are altmetrics indicators vulnerable and easy to