Author(s)

Sayan Roy^1,*, Hriddhiman Basu²

Affiliation(s)

¹ Indian Institute of Management Calcutta, India.

² Indian Institute of Management Mumbai, India.

Corresponding Author

Sayan Roy

ABSTRACT

In this study, word dynamics, co-occurring phrases, and keyword frequency in the area of androgenetic alopecia were thoroughly analysed during a ten-year period. The study tracks changes in word usage, frequency, and context and identifies variations in the distribution and usage patterns of keywords, subjects, and co-occurring phrases using natural language processing techniques and graph theory-based approaches. The research reveals hidden linkages and patterns between diverse ideas, shedding light on the co-occurrence patterns of numerous phrases in the literature on androgenetic alopecia. The study emphasises the value of clearly visualising and disseminating findings to a large audience. In order to communicate the findings of the 10-year trend analysis to patrons, legislatures, and other pertinent audiences, data visualisation tools, infographics, and reports are used. This makes sure that the results are useful, effective, and easily accessible so that they can guide the creation of policies and decisions pertaining to androgenetic alopecia. The results of this study may have substantial ramifications for academics, medical professionals, policymakers, and other industry participants. The research can lead future research paths, prioritise research areas, and suggest areas that require additional examination by highlighting key research challenges and pointing out gaps in the study of androgenetic alopecia. Identifying emergent study issues, analysing the changing patterns in the area, and establishing research strategies can all benefit from an analysis of word dynamics and correlations among terms. The results reveal hidden relationships and patterns, advance knowledge of the research environment in this area, and influence androgenetic alopecia research objectives and policies.

KEYWORDS

Hair loss; Androgenetic alopecia; Bibliometric; Text citation; Data visualizations

CITE THIS PAPER

Sayan Roy, Hriddhiman Basu. Beyond hair loss: exploring the evolution of androgenetic alopecia research based on text mining and bibliometrics. 2024, 2(3): 1-9. DOI: 10.61784/wms3010.

REFERENCES

[1] Rondanelli, Mariangela, Simone Perna, Gabriella Peroni, and Davide Guido. A Bibliometric Study of Scientific Literature in Scopus on Botanicals for Treatment of Androgenetic Alopecia. Journal of Cosmetic Dermatology. 2016, 15(2): 120–30. https://doi.org/10.1111/jocd.12198 

[2] Ruksiriwanich, Warintorn, Chiranan Khantham, Anurak Muangsanguan, Chuda Chittasupho, Pornchai Rachtanapun, Kittisak Jantanasakulwong, Yuthana Phimolsiripol, et al. Phytochemical Constitution, Anti-Inflammation, Anti-Androgen, and Hair Growth-Promoting Potential of Shallot (Allium Ascalonicum L.) Extract. Plants, 2022, 11(11): 1499. https://doi.org/10.3390/plants11111499 

[3] Han, Guangtao, Ting Liu, and Pengde Kang. Bibliometric Analysis of Ewing Sarcoma from 1993 to 2022. BMC Cancer, 2023, 23(1): 272. https://doi.org/10.1186/s12885-023-10723-7 

[4] Han, Yu, Sara A. Wennersten, and Maggie P. Y. Lam. Working the Literature Harder: What Can Text Mining and Bibliometric Analysis Reveal?. Expert Review of Proteomics, 2019, 16(11–12): 871–73. https://doi.org/10.1080/14789450.2019.1703678 

[5] Xiao, Lifei. A Bibliometric Analysis of Global Research Status and Trends in Neuromodulation Techniques in the Treatment of Autism Spectrum Disorder. 2023.

[6] Bragazzi, Nicola Luigi. Nanomedicine: Insights from a Bibliometrics-Based Analysis of Emerging Publishing and Research Trends. Medicina, 2019, 55(12): 785. https://doi.org/10.3390/medicina55120785

[7] Kirubalingam, Keshinisuthan, Agnieszka Dzioba, Yvonne Chan, and M. Elise Graham. Trends in Otolaryngology Publications: A 9-Year Bibliometric Analysis of Articles Published in Journal of Otolaryngology—Head and Neck Surgery. Journal of Otolaryngology - Head & Neck Surgery, 2023, 52(1): 17. https://doi.org/10.1186/s40463-022-00619-0

[8] Bai, Ming, Jingjing Zhang, De Chen, Mengying Lu, Junfen Li, Zheng Zhang, and Xiaowei Niu. Insights into Research on Myocardial Ischemia/Reperfusion Injury from 2012 to 2021: A Bibliometric Analysis. European Journal of Medical Research, 2023, 28(1): 17. https://doi.org/10.1186/s40001-022-00967-7

[9] Du, Xiaohan, and Yongjiang Hou. Hotspots Analysis and Perspectives of Prussian Blue Analogues (PBAs) in Environment and Energy in Recent 20 Years by CiteSpace. Environmental Science and Pollution Research, 2022,  30(5): 11141–74. https://doi.org/10.1007/s11356-022-24600-6

[10] Lei, Catherine, Frank A. De Stefano, Cody Heskett, Lane Fry, Kevin Le, Aaron Brake, Kevin Chatley, Jeremy Peterson, and Koji Ebersole. A Bibliometric Analysis of the Top 50 Most Influential Articles on External Ventricular Drains. World Neurosurgery, 2023, 172: 35–42. https://doi.org/10.1016/j.wneu.2023.01.040 

[11] Nabgan, Walid, M. Ikram, M. Alhassan, A.H.K. Owgi, Thuan Van Tran, L. Parashuram, A.H. Nordin, et al. Bibliometric Analysis and an Overview of the Application of the Non-Precious Materials for Pyrolysis Reaction of Plastic Waste. Arabian Journal of Chemistry, 2023, 16(6): 104717. https://doi.org/10.1016/j.arabjc.2023.104717 

[12] Hou, Junhui, Zongwei Lv, Yuan Wang, Xia Wang, Yibing Wang, and Kefeng Wang. Knowledge-Map Analysis of Percutaneous Nephrolithotomy (PNL) for Urolithiasis. Urolithiasis, 2023, 51(1): 34. https://doi.org/10.1007/s00240-023-01406-w