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 and investigate the evolution, growth, and maturity of thematic clusters in the field. Using the co-occurrence technique and analysis of thematic clusters is helpful to identify topic clusters that not only reveal the intellectual structure in the field but also is fruitful to gain a better understanding of the evolution, growth, and maturity of these topics. The article can not only highlight thematic gaps and avoid duplicate studies but also help us to better identify the basic trends, main topics, and popular topics in the field of citation bias. These analyses can serve as a powerful perspective for evaluating and improving research activities in this field. Moreover, investigating the evolution process of the topic network can show the improvement and developed/developing trends of the field and how the connections between topics change over time. Knowing the growth patterns of the topic network can help us to better understand the mechanisms of the network connections and links. Examining the maturity of the thematic network can provide us with information on how we can use this network to extract current trends or predict future trends. In addition, considering the dimensions of evolution, growth, and maturity of the topic network in the field of citation bias is very important and can help to develop our knowledge and understanding of the field to better classify thematic clusters.
Methodology: This applied research utilized the co-occurrence technique of words along with a scientometric approach. The research community includes all the keywords extracted from documents indexed in the English language on the Web of Science (WoS) database from 1965 to 2024. A database search was conducted through a researcher-made query containing prominent words and phrases related to the field of citation bias. Finally, 9739 documents were retrieved. Additionally, to visually represent the intellectual structure of this field, ‘VOSviewer’ (co-occurrence clustering) is used. Moreover, the R program, and BiblioShiny, the web-based interface of the Bibliometrix library were utilized to represent some maps. The strategic diagram (topic map), Sankey diagram (topic evolution), and Multiple Correspondence Analysis (MCA) were used to assess the maturity and evolution of the clusters.
Findings: The highest frequency of scientific productions is related to the subject categories of ‘general internal medicine’ and ‘library science and information science’. The United States ranks first in publishing scientific productions, followed by England, China, Canada, and Australia. The number of documents has increased between 2016 and 2022. Studies published in 2022 have more weight and importance in the network and include more relevant and prominent topics in this field. From 1965 to 2012, topics such as ‘citation analysis’ and ‘systematic review’ grew dramatically. Since 2013, the topic of ‘female’ was added to these themes, which indicates the increasing attention to gender inequality in science. The findings indicate the emergence of new trends in 2022-2018 and the emergence of new themes such as ‘machine learning’ and ‘bibliometrics’, which shows the impact of new technologies on citation and bias analysis. The annual growth rate of scientific production is %11.55. In other words, this growth rate shows how much the number of articles has increased each year. Also, the average number of citations received per article is 35.57. This means that each article has been cited an average of 35.57 times. The rate of co-authorship is 4.44, and the rate of international co-authorship is 29.35. The results of the factor analysis diagram based on the MCA method showed that themes such as ‘machine learning’, ‘bias’, ‘publication bias’, ‘citation’, ‘impact factor’, ‘research evaluation’, ‘network analysis’, ‘Bibliometric analysis’ has been the place of more focus and study in recent years in the field of citation bias.
Conclusion: The clusters resulting from the co-occurrence analysis are labeled: ‘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’.
The clusters with the themes of ‘bibliometrics’, ‘citation analysis’ and ‘bibliographic analysis’, ‘citation’, and ‘CiteSpace software’ are central clusters but immature and underdeveloped due to their placement in the fourth quadrant in the Strategic Diagram (SD). Clusters located in the second quadrant in the SD, such as ‘female’, ‘meta-analysis’, and ‘systematic review’, have strong internal relationships and a good level of maturity in this field due to their low centrality and high density. They are not central clusters, they are isolated but well-developed in the field of citation bias. No clusters have been placed in the first and third quadrants in the SD, which means that there are no mature, central, or emerging clusters in this field.