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Journal of Humanities, Arts and Social Science

ISSN Print: 2576-0556 Downloads: 1388841 Total View: 9391484
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Email: jhass@hillpublisher.com Citations: 301
ArticleOpen Access http://dx.doi.org/10.26855/jhass.2025.06.029

A Study on the Influencing Factors of Digital Inclusion of People in Rural Areas Based on Text Mining

Chunlan Dong1, Wenfeng Fan2, Keying Wang1, Shengxiang Liang3,4,*

1School of Humanities and Management, Guilin Medical University, Guilin 541004, Guangxi, China.

2School of Game Industry, Fuzhou Software Technology Vocational College, Fuzhou 350213, Fujian, China.

3Health Management Center, Guangxi Clinical Research Center for Diabetes and Metabolic Diseases, The Second Affiliated Hospital of Guilin Medical University, Guilin 541100, Guangxi, China.

4Guangxi Key Laboratory of Metabolic Reprogramming and Intelligent Medical Engineering for Chronic Diseases, Guilin Medical University, Guilin 541100, Guangxi, China.

*Corresponding author: Shengxiang Liang

Published: July 15,2025

Abstract

Objective: This study aims to explore the key factors influencing the digital in-clusion of rural residents by analyzing social media comments using the LDA topic model. Design/methodology/approach: The study employs Python technology to crawl comments related to rural digital inclusion from platform X. After preprocessing, 11,481 valid English texts were selected for analysis using the LDA model to identify potential influencing factors. Findings: The results reveal that the main factors influencing digital inclusion include technological infrastructure, social structure, government policies, and individual applications. These factors interact and collectively affect rural residents' digital access and participation. Originality/value: This study highlights the multidimensional factors at the technological, social, policy, and individual levels, providing valuable insights for future policy development. It emphasizes the importance of infrastructure in-vestment, digital literacy improvement, policy optimization, bridging the urban-rural digital divide, and promoting cross-sectoral collaboration to achieve comprehensive digital inclusion.

Keywords

Text mining; digital inclusion; rural areas; LDA model

References

Alonso, N., Vicent, L., & Trillo, D. (2024). Digitalisation and rural tourism development in Europe. Journal of Rural Studies.

Amara, A., Hadj Taieb, M. A., & Ben Aouicha, M. (2021). Multilingual topic modeling for tracking COVID-19 trends based on Facebook data analysis. Applied Intelligence, 51, 3052-3073.

An, Y., & Yan, Y. (2022). Intelligent retrieval method of library document information based on hidden topic mining. Web Intelligence, 20(2), 93-102. London, England: Sage UK.

BL New Delhi Bureau. (2025, March 21). Standing Committee on DoT recommends immediate resolution of bottlenecks in spectrum allocation, BharatNet execution. The Hindu BusinessLine. 

https://www.thehindubusinessline.com/news/standing-committee-on-dot-recommends-immediate-resolution-of-bottlenecks-in-spectrum-allocation-bharatnet-execution/article69358982.ece

Cheng, W., Yang, J., Wu, X., Zhang, T., & Yin, Z. (2024). A quantitative study on factors influencing user satisfaction of micro-mobility in China in the post-sharing era. Sustainability, 16(4), 1637. 

https://doi.org/10.3390/su16041637

Correa, T., & Pavez, I. (2016). Digital inclusion in rural areas: A qualitative exploration of challenges faced by people from isolated communities. Journal of Computer-Mediated Communication, 21(3), 247-263.

Deng, J., Li, X., & Zhang, N. (2024). The impact of digital rural construction on rural revitalization—Empirical evidence from Chinese county panel data. Agriculture, 14(11), 1903.

Diaz-Garcia, J. A., Ruiz, M. D., & Martin-Bautista, M. J. (2023). A survey on the use of association rules mining techniques in textual social media. Artificial Intelligence Review, 56(2), 1175-1200.

Guo, S., & Zhang, G. (2023). Comparisons of the Economist topics on three countries from 1991 through 2016. Libri, 73(1), 37-50.

Huisman, M., & van Dijk, J. (2021). The digital divide. Communications, 46(4), 611-612.
https://doi.org/10.1515/commun-2020-0026

IANS. (2025, March 27). 2.18 lakh gram panchayats have BharatNet link for high-speed Internet services: Minister. Indiaglitz.
https://www.indiaglitz.com

Javed, R. T., Nasir, O., Borit, M., Vanhée, L., Zea, E., Gupta, S., et al. (2022). Get out of the BAG! Silos in AI ethics education: Unsupervised topic modeling analysis of global AI curricula. Journal of Artificial Intelligence Research, 73, 933-965.

Kim, M., Park, Y., & Yoon, J. (2016). Generating patent development maps for technology monitoring using semantic patent-topic analysis. Computers & Industrial Engineering, 98, 289-299.

Liu, S., Zhu, S., Hou, Z., et al. (2023). Digital village construction, human capital, and the development of the rural older adult care service industry. Frontiers in Public Health, 11, 1190757.

Liu, Z., Shan, S., & Shao, B. (2023). Research on public information needs and public library information service strategies in the post epidemic era. Intelligence Science, 41(7), 179-188. 

https://doi.org/10.13833/j.issn.1007-7634.2023.07.021

Lv, K., Xiang, M., & Jing, J. (2023). A new species of the genus Lepidoptera (Coleoptera, Staphylinidae) from China. Library and Intelligence Work, 67(12), 89-102. 

https://doi.org/10.13266/j.issn.0252-3116.2023.12.009

Meng, X., Wang, X., Nisar, U., et al. (2023). Mechanisms and heterogeneity in the construction of network infrastructure to help rural households bridge the "digital divide." Scientific Reports, 13(1), 19283.

Mimno, D., Wallach, H., Talley, E., Leenders, M., & McCallum, A. (2011). Optimizing semantic coherence in topic models. In Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing (pp. 262-272). ACL: Stroudsburg, PA, USA.

Oraki, A. H., Fadavi, M. S., & Saeidian Khorasgani, N. (2020). Identifying the components of distance education in rural areas to provide a distance education model for secondary schools in villages of Iran. Iranian Journal of Educational Sociology, 3(3), 145-157.

Ordonez, T. N., Yassuda, M. S., & Cachioni, M. (2011). Elderly online: Effects of a digital inclusion program in cognitive performance. Archives of Gerontology and Geriatrics, 53(2), 216-219. 

https://doi.org/10.1016/j.archger.2010.11.007

Paek, S., Um, T., & Kim, N. (2021). Exploring latent topics and international research trends in competency-based education using topic modeling. Education Sciences, 11(6), 303.

Pan, J. (2019). How Chinese officials use the Internet to construct their public image. Political Science Research and Methods, 7(2), 197-213.

Potter, A. J. M., Ward, M. M. P., Natafgi, N. M., et al. (2016). Perceptions of the benefits of telemedicine in rural commu-nities. Perspectives in Health Information Management, 13(1), 1-13.

Roberts, E., Anderson, B. A., Skerratt, S., et al. (2017). A review of the rural-digital policy agenda from a community resilience perspective. Journal of Rural Studies, 54, 372-385.

Röder, M., Both, A., & Hinneburg, A. (2015). Exploring the space of topic coherence measures. In Proceedings of the eighth ACM International Conference on Web Search and Data Mining (pp. 399-408). ACM: New York, NY, USA.

Sieck, C. J., Sheon, A., Ancker, J. S., et al. (2021). Digital inclusion as a social determinant of health. NPJ Digital Medicine, 4(1), 1-7.

Warschauer, M. (2003). Technology and social inclusion: Rethinking the digital divide. Cambridge, USA: MIT Press.

https://doi.org/10.7551/mitpress/6699.001.0001

Xie, X., Li, D., Zhu, W., Zhang, L., Du, X., & Wang, H. (2022). Drug efficacy prediction in tumors based on LDA model. In 2022 41st Chinese Control Conference (CCC) (pp. 5747-5752). IEEE: New York, NY, USA.

Zhang, D., & Zhang, M. (2022). A review of research progress in the application of LDA topic modelling in graphical intelligence domain. Library Intelligence Knowledge, 6, 143-157. 

https://doi.org/10.13366/j.dik.2022.06.143

Zhang, Y. (2023). Measuring and applying digital literacy: Implications for access for the elderly in rural China. Education and Information Technologies, 28(8), 9509-9528.

Zhou, J., Yu, L., & Choguill, C. L. (2021). Co-evolution of technology and rural society: The blossoming of Taobao villages in the information era, China. Journal of Rural Studies, 83, 81-87.


How to cite this paper

A Study on the Influencing Factors of Digital Inclusion of People in Rural Areas Based on Text Mining

How to cite this paper: Chunlan Dong, Wenfeng Fan, Keying Wang, Shengxiang Liang. (2025) A Study on the Influencing Factors of Digital Inclusion of People in Rural Areas Based on Text Mining. Journal of Humanities, Arts and Social Science9(6), 1234-1243.

DOI: http://dx.doi.org/10.26855/jhass.2025.06.029