A bibliometric analysis of worldwide cancer research using machine learning methods

Lin, Lianghong; Liang, Likeng; Wang, Maojie; Huang, Run‐Yue; Gong, Mengchun; Song, Gaofeng; Hao, Tianyong

doi:10.1002/cai2.68

Cited by 1 publication

(1 citation statement)

References 39 publications

(42 reference statements)

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“…In terms of affiliations, the University of Pennsylvania occupies the first position based on the number of papers published around the area of natural language processing in social media research. Its relevance within the scientific community, as well as its exceptional productivity, are further supported by its presence in the top for other bibliometric studies, such as the following ones related to social media research [49], machine learning for mental health in social media [50], machine learning used in cancer research [51], innovation research [52], corporate social responsibility [53], or in general scientific rankings made for specific periods, such as the one conducted by Yan and Zhiping for the 1970-2020 period on the WoS database [54].…”

Section: Discussionmentioning

confidence: 96%

A Bibliometric Analysis of Text Mining: Exploring the Use of Natural Language Processing in Social Media Research

Sandu,

Cotfas,

Stănescu

et al. 2024

Applied Sciences

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Natural language processing (NLP) plays a pivotal role in modern life by enabling computers to comprehend, analyze, and respond to human language meaningfully, thereby offering exciting new opportunities. As social media platforms experience a surge in global usage, the imperative to capture and better understand the messages disseminated within these networks becomes increasingly crucial. Moreover, the occurrence of adverse events, such as the emergence of a pandemic or conflicts in various parts of the world, heightens social media users’ inclinations towards these platforms. In this context, this paper aims to explore the scientific literature dedicated to the utilization of NLP in social media research, with the goal of highlighting trends, keywords, and collaborative networks within the authorship that contribute to the proliferation of papers in this field. To achieve this objective, we extracted and analyzed 1852 papers from the ISI Web of Science database. An initial observation reveals a remarkable annual growth rate of 62.18%, underscoring the heightened interest of the academic community in this domain. This paper includes an n-gram analysis and a review of the most cited papers in the extracted database, offering a comprehensive bibliometric analysis. The insights gained from these efforts provide essential perspectives and contribute to identifying pertinent issues in social media analysis addressed through the application of NLP.

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