Research on the relationships between discourse leading indicators and citations: perspectives from altmetrics indicators of international multidisciplinary academic journals

Wang, Xu; Feng, Xin

doi:10.1108/lht-09-2021-0296

Cited by 10 publications

(9 citation statements)

References 71 publications

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“…It uses statistical methods to describe, analyze, evaluate, and predict the development status and trends of research objects. Bibliometrics has been widely used in analyzing disciplinary structures and development trends [37]. Knowledge maps are an information visualization tool to systematically and comprehensively analyze the research status, hotspots, and future trends of specific research fields based on bibliometrics [38][39][40].…”

Section: Analysis Methodsmentioning

confidence: 99%

Advances and future trends in research on carbon emissions reduction in China from the perspective of bibliometrics

Chen

Liu

2023

PLoS ONE

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Addressing global warming is one of the most pressing environmental challenges and a crucial agenda for humanity. In this literature study, we employed bibliometrics to reproduce nearly two decades of research on carbon emission reduction in China, the largest carbon emitter worldwide. The scientometrics analysis was conducted on 1570 academic works published between 2001 and 2021 concerning China’s carbon emission reduction to characterize the knowledge landscape. Using CiteSpace and VOSviewer, the basic characteristics, research forces, knowledge base, research topic evolution, and research hotspots were identified and revealed. The analysis results show that the attention to and research on China’s carbon emissions have increased in recent years, giving rise to leading institutions and relatively stable core journal groups in this field. The research disciplines are relatively concentrated, but the research collaboration needs strengthening. The research hotspots are mainly carbon emission causes, impacts, and countermeasures in China, and the research frontiers have been constantly advanced and expanded. In the future, research on countermeasures needs more effort, and research cooperation needs to strengthen. The changing landscape of hotspot clusters reveals China’s transition towards a low-carbon economy. Through comprehensive analysis of the potential and obstacles to China’s transition to low-carbon development, we identified three promising areas of action (low-carbon cities, low-carbon technologies and industries, and transforming China’s energy system) and proposed research directions to address remaining gaps systematically.

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Section: Analysis Methodsmentioning

confidence: 99%

Advances and future trends in research on carbon emissions reduction in China from the perspective of bibliometrics

Chen

Liu

2023

PLoS ONE

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“…Then, how to scientifically and objectively evaluate the quality of Sci-Tech journals becomes crucial. Since Pinski [7] proposed the journal evaluation rules, various methods and indicators for evaluating the academic quality of Sci-Tech journals have emerged [8]. Traditional methods include citation analysis, quantitative analysis based on fuzzy mathematical theorem, and analytic hierarchy method [9].…”

Section: Related Workmentioning

confidence: 99%

Study on Contribution of Different Journal Evaluation Indicators to Impact Factor Based on Machine Learning

Ma,

Han,

Zeng

et al. 2023

Scientific Programming

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Sci-Tech journals have long served as platforms for academic communication and the collision of ideas, facilitating advanced inventions and major discoveries in science. The speed of development and future prospects of a field in the current era can often be reflected by the quality and quantity of cutting-edge papers published in Sci-Tech journals within that field. Currently, the impact factor of Sci-Tech journals is a widely recognized journal evaluation index that comprehensively reflects the quality and influence of the journals under evaluation. However, traditional journal evaluation methods based on statistical formulas, while relatively simple and fast, have certain limitations. They are not comprehensive enough and do not support the comparison between journals from different disciplines. In recent times, researchers have delved into using multiple suitable indicators for comprehensive journal evaluation, attempting to understand the role each indicator plays in the evaluation process, such as the rank sum ratio. Our paper presents a new dataset constructed from data from journals across various fields obtained from the China Wanfang Literature Platform. We endeavor to explore a series of novel journal evaluation methods based on machine learning, including deep learning models. With these 9 methods, we aim to determine the contribution of 17 journal evaluation indicators to the impact factor and identify important factors that can further enhance the quality and influence of Sci-Tech journals, which has great guiding significance for the future development of journals.

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“…The social Q&A community attaches importance to the construction of social relations. Users can not only form Q&A links with each other but also interact through social behaviours, such as liking, following and sharing (Feng et al , 2022; Wang and Feng, 2022), which attracts more and more people to participate in the process of knowledge dissemination and sharing. The efficiency of knowledge sharing has been greatly improved, and the total amount of knowledge has grown exponentially (Wang, 2022a, 2022b).…”

Section: Introductionmentioning

confidence: 99%

Analysis of the characteristics and evolution of knowledge label networks in the Q&A community: taking the Zhihu platform as an example

Feng

Wang

Xue

et al. 2023

Self Cite

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Purpose In the era of mobile internet, the social Q&A community has built a large-scale and complex knowledge label network through its internal knowledge units, and the scale and structure of the network have changed over time. By analysing the structural characteristics and evolution rules of knowledge label networks, the main purpose of this study is to understand the internal mechanisms of the replacement of old and new knowledge and the expansion of knowledge element boundaries, so as to explore the realization path of knowledge management in the new era from the perspective of complex networks. Design/methodology/approach This paper uses distributed crawlers to capture 419,349 samples from the Zhihu platform. Each sample contains 33 characteristic dimensions, and the natural year is used as the sliding window to divide the whole. In this study, the global knowledge label network and 11 local knowledge label networks are first constructed. Then, the degree distribution analysis and central node exploration of the knowledge label network are carried out using the complex network method. Finally, the average shortest path and average clustering coefficient of the network are analysed by the time series method, and the ARIMA model is used to predict the evolution of the correlation coefficient. Findings The research results show that the dissimilation degree of the degree distribution of the knowledge label network has gradually decreased from 2011 to 2021, and the attention of users in the knowledge community has shown a trend of distraction and diversification over time. With the expansion of the scale of the knowledge label network and the transformation to an information network, the network sparsity is becoming more and more obvious, and the knowledge granularity of the Q&A community is being refined and diversified. The prediction of the correlation coefficient of the knowledge label network by the ARIMA model shows that the connection between the labels is lacking diversity and the opinion strengthening phenomenon tends to strengthen, which is more likely to form the “echo chamber effect”, resulting in mutual isolation and even opposition between different circles. The Q&A community is about to enter a mature stage, and the corresponding status of each label has been finalized. The future development trend of label networks will be reflected in the substitution between labels, and the specific structure will not change significantly. Originality/value The Q&A community model is the trend in Web 2.0 community development. This study proves the effectiveness of complex networks and time series prediction methods in knowledge label network mining in the Q&A community.

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Research on the relationships between discourse leading indicators and citations: perspectives from altmetrics indicators of international multidisciplinary academic journals

Cited by 10 publications

References 71 publications

Advances and future trends in research on carbon emissions reduction in China from the perspective of bibliometrics

Advances and future trends in research on carbon emissions reduction in China from the perspective of bibliometrics

Study on Contribution of Different Journal Evaluation Indicators to Impact Factor Based on Machine Learning

Analysis of the characteristics and evolution of knowledge label networks in the Q&A community: taking the Zhihu platform as an example

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