Scientometrics and knowledge flow analysis should be standardized

After reading a recent study “Trends of mapping knowledge structure and themes of cancer sonodynamic therapy: a text-mining study” by Wu et al. (1) published in this journal, I would like to offer some suggestions

Wu et al. vividly and beautifully presented the hot issues and research trends in the field of cancer sonodynamic therapy, including the regional and temporal concentration of research, in the article, which is impressive.

But the key points worth criticizing still exist. For example, firstly, the text mining paradigm mentioned in the title of this article is confusing. Text mining analysis requires processing structured and unstructured text, and then using machine learning methods to model and predict word frequency, in order to achieve sentiment analysis, topic modeling, and correlation interpretation (2). This method focuses on interpreting the content of the article. However, as mentioned in the original text, this study used bibliometric methods and analyzed the authors, their institutions, and countries, as well as the citation status such as co-citation data extraction and keyword analysis. Among them, keyword analysis seems to have emotional similarities with text mining. But in fact, these keywords are not obtained through in-depth analysis of the text, but through the analysis of the keyword column added and submitted by the author in the submission system and the platform’s content evaluation. This is not about completing text mining, but about extracting and visualizing the given keywords, which also falls within the scope of bibliometric analysis.

Secondly, in order to consider this study as a bibliometric analysis, the extensive research in this article has led to some regrettable omissions and misunderstandings of information. The author’s original text was “Figure 4 Illustrated empirical map of journal cancer SDT” (1). However, the actual meaning of this figure, as the author stated, is to analyze the flow and evolution of the entire research scope’s knowledge domain through the mapping relationship between journals and knowledge domains, rather than illustrating that knowledge is limited to journals. At the same time, the author’s judgment method for the degree of evolution and flow between different knowledge fields is that the yellow line is relatively wide, which may not be strict enough or even incorrect. Based on the images provided by the author, it can be concluded that the z-index of knowledge flow between the fields of “Physcics, Materials, Chemistry” and “Chemistry, Materials, Physcics” based on citation frequency is the only one that exceeds 4, which is 4.401875. This is the most significant knowledge flow that the author can obtain but did not specify. The ratio of the longitudinal and transverse diameters of ellipses on the periphery of the knowledge domain, as well as the size of the ellipses and the values above them, provide good clues to the abundance of knowledge within the domain.

Finally, as the author stated, bibliometric methods are excellent means of objectively structuring and presenting cutting-edge data in the field of knowledge. The author has produced numerous and exquisite work results, predicting the research status and future trends in this field through citation explosion and keyword explosion

Appendix 1: Response to “Scientometrics and knowledge flow analysis should be standardized”.

Acknowledgments

None.

Footnote

Funding: None.

Conflicts of Interest: The author has completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-2024-2958/coif). The author has no conflicts of interest to declare.

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References

1. Wu H, Cheng K, Sun Z, Lu Y, Guo Q, Li C. Trends of mapping knowledge structure and themes of cancer sonodynamic therapy: a text-mining study. Quant Imaging Med Surg 2024;14:8734-57. [Crossref] [PubMed]
2. Ong SQ, Ahmad H. Tracking mosquito-borne diseases via social media: a machine learning approach to topic modelling and sentiment analysis. PeerJ 2024;12:e17045. [Crossref] [PubMed]