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The COVID-19 epidemic has brought attention to the variables that influence the mental health of health workers who are entrusted with nursing individuals. Despite the fact that many articles have examined the effects of social media usage on mental health, there is a lack of research synthesizing learning from this body of research. The purpose of this study is to use text mining and citation-based bibliometric analysis to conduct a detailed review of extant literature on health workers’ mental health and social networking habits.

This study conducts a full-text analysis of 36 articles selected on health workers' mental health and social media using text-mining techniques in R programming and a bibliometric citation analysis of 183 papers from the Scopus database in VOS viewer software. But the limitations of the methods used in this study are that the bibliometric analysis was limited to the Scopus database because the VOS viewer program did not support any other database and the text-mining approach caused the natural processing redundancy.

The bibliometric analysis reveals the thematic networks that exist in the literature of health workers’ mental health and social networking. The findings from text mining identified ten topic models, which helped to find the related papers classified in ten different groups and are provided alongside a summary of the published research and a list of the primary authors with posterior probability through Latent Dirichlet Allocation.

To the best of the authors’ knowledge, this is the first hybrid review, combining text mining and bibliometric review, on health workers’ mental health where social networking plays a moderating role. This paper critically provides an overview of the impact of social networking on health workers' mental health, presents the most important and frequent topics, introduces the scientific visualization of articles published in the Scopus database and suggests further research avenues. These findings are important for academics, health practitioners and medical specialists interested in learning how to better support the mental health of health workers using social media.

Although there has been a significant amount of research on Smart Tourism, the articles have not yet been combined into a thorough literature review that can examine research streams and the scope of future research. The purpose of this study is to examine the literature on the impact of deploying the Internet of Things (IoT) in tourism sector development to attract more visitors using a text mining technique and citation based bibliometric analysis for the first time.

This study uses R programming to do a full-text analysis of 36 publications on IoT in tourism and visualization of similarities viewer software to conduct a bibliometric citation analysis of 469 papers from the Scopus database. Aside from that, the documents were subjected to a longitudinal study using Excel and word frequency using a trending topic using the R-tool.

Results from the bibliometric study revealed the networks that exist in the literature of Tourism Management. With the use of log-likelihood, the findings from text mining identified nine theme models on the basis of relevancy, which is presented alongside an overview of the existing papers and a list of the primary authors with posterior probability using latent Dirichlet allocation.

This study examines tourism literature in which IoT plays a significant role. To the best of the authors’ knowledge, this study is the first to combine text mining with a bibliometric review. It significantly analyzes and discusses the impact of technology in the tourism sector development on attracting tourists while presenting the most important and frequently discussed topics and research in these writings. These findings provide researchers, tourism managers and technology professionals with a complete understanding of e-tourism and to provide smart devices to attract tourists.

Aunque ha habido un número importante de estudios sobre el turismo inteligente, todavía no se dispone de una revisión bibliográfica exhaustiva que permita examinar las corrientes de investigación y las sugerencias de investigación futuras. Este estudio examina la literatura sobre el impacto del Internet de las cosas en el desarrollo del sector turístico para atraer más visitantes utilizando una técnica de minería de textos y un análisis bibliométrico basado en citas.

Este estudio utiliza la programación R para hacer un análisis de texto completo de 36 publicaciones sobre IoT en el turismo y el software de visualización de similitudes (VOS) para realizar un análisis bibliométrico de citas de 469 documentos de la base de datos Scopus. Además, los documentos fueron sometidos a un estudio longitudinal mediante Excel y a la frecuencia de palabras mediante un tema de tendencia utilizando la herramienta R.

Los resultados del estudio bibliométrico revelaron las redes existentes en la literatura de la Gestión Turística. Con el uso de la log-verosimilitud, los resultados de la minería de textos identificaron nueve modelos temáticos sobre la base de la relevancia, que se presentan junto con una visión general de los documentos existentes y una lista de los autores principales con probabilidad posterior utilizando la asignación latente de dirichlets.

Este estudio examina la literatura sobre turismo en la que la IoT desempeña un papel importante. Este estudio es el primero que combina la minería de textos con una revisión bibliométrica. Analiza y discute de forma significativa el impacto de la tecnología en el desarrollo del sector turístico para atraer a los turistas, a la vez que presenta los temas e investigaciones más importantes y más frecuentemente discutidos en estos escritos. Estos resultados proporcionan a los investigadores, gestores turísticos y profesionales de la tecnología una comprensión integral del turismo electrónico y los dispositivos inteligentes para atraer a los turistas.

虽然已经有大量关于智慧旅游的研究, 但这些文章尚未整合成一个全面的文献综述, 可以检阅目前的研究流和未来研究的范畴。本研究首次使用文本挖掘技术和基于引文的文献计量分析, 来研究有关在旅游业发展中部署物联网对吸引更多游客的影响的文献。

本研究使用R编程对36篇关于旅游业物联网的文章进行全文分析, 并使用相似性可视化（VOS）查看器软件对Scopus数据库中的469篇论文进行文献计量引文分析。除此之外, 还利用Excel对这些文献进行了纵向研究, 并使用R工具对趋势主题进行了词频分析。

文献计量研究的结果揭示了旅游管理文献中现有的网络。通过使用对数似然, 文本挖掘的结果根据相关性确定了9个主题模型, 这些模型与现有论文的概述和主要作者名单在使用潜在狄里奇分配（LDA）的后验概率一起呈现。

本研究对旅游物联网相关文献进行了分析研究, 它首次将文本挖掘与文献计量学审查相结合。这项研究着重分析和讨论了技术在旅游行业发展中对吸引游客的影响, 同时介绍了这些文章中最重要和经常讨论的主题和研究。这些发现为研究人员、旅游管理者和技术专家提供了对科技与旅游的全面了解, 并提供关于智能设备来吸引游客的建议。