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The purpose of this paper is to conduct qualitative research for research fronts of international entrepreneurship education, based on quantitative analysis for visualization mapping in a new research perspective, to make a study platform in the field of international entrepreneurship education.

In total, 2,000 bibliographic data in the field of international entrepreneurship education were retrieved from Science Citation Index‐Expanded (SCI‐E) and Social Science Citation Index (SSCI), which are Journal Citation Index databases in Web of Science (WOS), for the period 1945 to 2010. Having combined quantitative analysis with qualitative thinking, the international emerging CiteSpace software and visualization method were adopted for processing bibliographic data and drawing knowledge mapping.

The authors conclude that for more than half a century, the research fronts of international entrepreneurship education have focused on Holland theory; entrepreneurial self‐efficacy, role modeling and start‐up business performance; historical examination and application of entrepreneurship education; entrepreneurship education pedagogy, discipline and program; occupational aspiration and choice; high‐school career education, social‐cognitive variable and self‐efficacy treatment; distance education course and career planning; educational productivity; career advancement and maturity.

The bibliographic data and visualization method ensure the originality of this paper, which contributes to develop a new research perspective and study platform in the field of international entrepreneurship education. Furthermore, research directions and academic clues have been provided for teaching and research staff engaged in entrepreneurship education in China.

Social mediametrics is a subfield of measurement in which the emphasis is placed on social media data. This paper analyzes the trends and patterns of paper comprehensively mentions on Twitter, with a particular focus on Twitter's mention behaviors. It uncovers the dissemination patterns and impact of academic literature on social media. The research has significant theoretical and practical implications.

This paper explores the fundamental attributes of Twitter mentions by means of analyzing 9,476 pieces of scholarly literature (5,097 from Nature and 4,379 from Science), 1,474,898 tweets and 451,567 user information collected from Altmetric.com database and Twitter API. The study uncovers assorted Twitter mention characteristics, mention behavior patterns and data accumulation patterns.

The findings illustrate that the top academic journals on Twitter have a wider range of coverage and display similar distribution patterns to other academic communication platforms. A large number of mentioners remain unidentified, and the distribution of follower counts among the mention users exhibits a significant Pareto effect, indicating a small group of highly influential users who generate numerous mentions. Furthermore, the proportion of sharing and exchange mentions positively correlates with the number of user followers, while the incidence of supportive mentions has a negative correlation. In terms of country-specific mention behavior, Thai scholars tend to utilize supportive mentions more frequently, whereas Korean scholars prefer sharing mentions over communicating mentions. The cumulative pattern of Twitter mentions suggests that these occur before official publication, with a half-life of 6.02 days and a considerable reduction in the number of mentions is observed on the seventh day after publication.

Conducting a multi-dimensional and systematic analysis of Twitter mentions of scholarly articles can aid in comprehending and utilizing social media communication patterns. This analysis can uncover literature's distribution patterns, dissemination effects and social significance in social media.

This paper aims to reveal the interdisciplinarity of information science (IS) from the perspective of the evolution of theory application.

Select eight representative IS journals as data sources, extract the theories mentioned in the full texts of the research papers and then measure annual interdisciplinarity of IS by conducting theory co-occurrence network analysis, diversity measure and evolution analysis.

As a young and vibrant discipline, IS has been continuously absorbing and internalizing external theoretical knowledge and thus formed a high degree of interdisciplinarity. With the continuous application of some kernel theories, the interdisciplinarity of IS appears to be decreasing and gradually converging into a few neighboring disciplines. Influenced by big data and artificial intelligence, the research paradigm of IS is shifting from a theory centered one to a technology centered one.

This study helps to understand the evolution of the interdisciplinarity of IS in the past 21 years. The main limitation is that the data were collected from eight journals indexed by the Social Sciences Citation Index and a small amount of theories might have been omitted.

This study identifies the kernel theories in IS research, measures the interdisciplinarity of IS based on the evolution of the co-occurrence network of theory source disciplines and reveals the paradigm shift being happening in IS.