Evolution, Growth, and Maturity of the Thematic Network in the field of Citation Bias

purpose: The research aimed to map and analyze the co-word network in the field of citation bias, as well as to investigate the evolution, growth, and maturity of thematic clusters within this domain. By employing the co-occurrence technique and thematic cluster analysis, the study identifies topic clusters and reveal the intellectual structure of the field and provide valuable insights into the development and maturation of these topics. The findings not only highlight thematic gaps and prevent redundant studies but also elucidate fundamental trends, primary topics, and popular themes within citation bias research. These analyses provide a valuable perspective for evaluating and enhancing research activities in this field. Furthermore, examining the evolution of the topic network can reveal the advancements and emerging trends within the field, as well as how the relationships between topics change over time. Understanding the growth patterns of the thematic network can enhance our comprehension of the mechanisms underlying its connections and links. Similarly, analyzing the maturity of the thematic network can provide valuable insights into how we can leverage this network to identify current trends and predict future developments. By integrating the dimensions of evolution, growth, and maturity within the citation bias topic network, this study deepens our understanding of the field and improves our ability to classify and interpret thematic clusters effectively.Methodology: This applied research employed a co-occurrence technique for analyzing words, combined with a scientometric approach. The research community encompasses all keywords extracted from documents indexed in the English language within the Web of Science (WoS) database from 1965 to 2024. A database search was performed using a researcher-developed query that included significant words and phrases pertinent to the field of citation bias. Finally, 9,739 documents were retrieved. Additionally, to visually represent the intellectual structure of this field, VOSviewer (a co-occurrence clustering tool) was employed. Furthermore, the R programming language and BiblioShiny, the web based interface of the Bibliometrix library, were utilized to create various maps. The strategic diagram (topic map), Sankey diagram (topic evolution), and Multiple Correspondence Analysis (MCA) were utilized to evaluate the maturity and evolution of the clusters.Findings: The highest frequency of scientific publications is associated with the subject categories of 'general internal medicine' and 'library science and information science.' The United States leads in scientific research output, followed by England, China, Canada, and Australia. The number of published documents has steadily increased from 2016 to 2022, with Studies published in 2022 holding greater significance within the network and encompassing more prominent and relevant topics in the field. From 1965 to 2012, dominant themes included ‘citation analysis’ and ‘systematic review’. Since 2013, the topic of ‘female’ emerged, reflecting growing attention to gender inequality in science. From 2018 to 2022, new trends have emerged, highlighting themes such as machine learning and bibliometrics, which underscore the influence of new technologies on citation analysis and bias assessment. The annual growth rate of scientific production is 11.55% indicating a consistent yearly increase in the number of articles. Additionally, the average number of citations per article is 35.57, demonstrating the impact of research in this field. The co-authorship rate is 4.44, with international co-authorship accounting for 29.35% of collaborations. The results of the factor analysis diagram, based on the Multiple Correspondence Analysis (MCA), reveal that themes such as ‘machine learning’, ‘bias’, ‘publication bias’, ‘citation’, ‘impact factor’, ‘research evaluation’, ‘network analysis’, ‘Bibliometric analysis’ have received significant attention in recent years within the field of citation bias.Conclusion: The clusters identified through the co-occurrence analysis are labeled as follows: ‘Citation Bias and Gender Inequality,’ ‘Citation Analysis through Bibliometric Analysis and Visualization,’ ‘Citation Metrics and Bias,’ ‘Trend Analysis through Citation-Based Databases,’ ‘Investigation of Citation Bias through Systematic Review and Meta-Analysis,’ ‘Analysis of Citation Bias through Machine Learning and Artificial Intelligence,’ and ‘Gender Disparities in Citation Bias.’ Among these, the clusters related to 'bibliometrics,' 'citation analysis,' 'bibliographic analysis,' 'citation,' and 'CiteSpace software' are central to the study. However, they remain immature and underdeveloped, as evidenced by their position in the fourth quadrant of the Strategic Diagram (SD). Clusters situated in the second quadrant of the SD, such as ‘female’, ‘meta-analysis’, and ‘systematic review’, exhibit strong internal relationships and a high level of maturity, as indicated by their low centrality and high density. These clusters are not central, but are well-developed and somewhat isolated within the field of citation bias. Notably, no clusters are positioned in the first and third quadrants of the SD, indicating the absence of mature, central, or emerging clusters in this field.

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