A Machine Learning–Based Bibliometric Analysis of Global Research Trends on Loneliness in Older Adults

Sri Susanty; Yeu-Hui Chuang; Hsiao-Yean Chiu; Faizul Hasan

doi:10.12688/f1000research.182051.1

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Research Article

A Machine Learning–Based Bibliometric Analysis of Global Research Trends on Loneliness in Older Adults

[version 1; peer review: awaiting peer review]

Sri Susanty^1,2, Yeu-Hui Chuang^3-5, Hsiao-Yean Chiu^3,6-8, Faizul Hasan ¹

PUBLISHED 26 May 2026

Author details Author details

¹ Faculty of Nursing, Chulalongkorn University, Bangkok, Bangkok, Thailand
² Nurse Professional Education Study Program, Faculty of Medicine, Universitas Halu Oleo, Kendari, South East Sulawesi, Indonesia
³ School of Nursing, College of Nursing, Taipei Medical University, Taipei City, Taipei City, Taiwan
⁴ Department of Nursing, Wan Fang Hospital, Taipei Medical University, Taipei City, Taipei City, Taiwan
⁵ Research Center in Nursing Clinical Practice, Wan Fang Hospital, Taipei Medical University, Taipei City, Taipei City, Taiwan
⁶ Research Center of Sleep Medicine, College of Medicine, Taipei Medical University, Taipei City, Taipei City, Taiwan
⁷ Department of Nursing, Taipei Medical University Hospital, Taipei City, Taipei City, Taiwan
⁸ Research Center of Sleep Medicine, Taipei Medical University Hospital, Taipei City, Taipei City, Taiwan

Sri Susanty
Roles: Conceptualization, Data Curation, Formal Analysis, Funding Acquisition, Investigation, Methodology, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

Yeu-Hui Chuang
Roles: Supervision, Validation, Writing – Original Draft Preparation, Writing – Review & Editing

Hsiao-Yean Chiu
Roles: Supervision, Validation, Writing – Original Draft Preparation, Writing – Review & Editing

Faizul Hasan
Roles: Conceptualization, Data Curation, Formal Analysis, Funding Acquisition, Investigation, Methodology, Project Administration, Resources, Software, Supervision, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

OPEN PEER REVIEW

REVIEWER STATUS AWAITING PEER REVIEW

This article is included in the Artificial Intelligence and Machine Learning gateway.

This article is included in the Dignity in Aging collection.

Abstract

Background

Loneliness in the elderly presents considerable physical and emotional health hazards, constituting a significant public health issue. This study utilizes bibliometric analysis to investigate global research trends, collaborations, and emerging themes in the application of machine learning to mitigate elderly loneliness.

Methods

A thorough examination of the Scopus database was performed for publications spanning from January 1, 2014, to December 31, 2024. The analysis used R packages like Bibliometrix and VOSviewer to look at things like citation metrics, trends in publications, partnerships between institutions, keyword co-occurrences, and networks of co-authors.

Results

Findings indicate an increasing interest in machine learning methodologies to address loneliness among the elderly, with the United States, China, and Canada at the forefront of publication output. Social media usage, depression, and predictive modeling were among the new themes that emerged, along with important writers and research groups.

Conclusions

This study gives us important information about how ML research for the well-being of older people is changing and can help us come up with new ways to reduce loneliness in older people.

Keywords

Loneliness, elderly, machine learning, health and well-being, artificial intelligence

Corresponding author: Faizul Hasan

Competing interests: No competing interests were disclosed.

Grant information: This research was supported by the Second Century Fund (C2F), Chulalongkorn University, Bangkok, Thailand. Funding recipient: Sri Susanty.
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Copyright: © 2026 Susanty S et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

How to cite: Susanty S, Chuang YH, Chiu HY and Hasan F. A Machine Learning–Based Bibliometric Analysis of Global Research Trends on Loneliness in Older Adults [version 1; peer review: awaiting peer review]. F1000Research 2026, 15:798 (https://doi.org/10.12688/f1000research.182051.1) First published: 26 May 2026, 15:798 (https://doi.org/10.12688/f1000research.182051.1) Latest published: 26 May 2026, 15:798 (https://doi.org/10.12688/f1000research.182051.1)

Introduction

Geriatric loneliness (GL) has emerged as a significant public health concern owing to its potential adverse effects on mental and physical health.^1,2,3 The increasing prevalence of loneliness among older individuals globally, as the population ages, has been associated with several adverse outcomes, including depression, cognitive decline, and a diminished quality of life.^4,5,6 Policymakers, researchers, and healthcare providers currently prioritize the resolution of this issue.

Recent advancements in machine learning (ML) techniques have shown promise in identifying and addressing loneliness among the elderly.⁷ These methods provide insights into the underlying causes and potential therapies for loneliness by analyzing large databases, identifying patterns, and determining risk factors.⁸ Moreover, machine learning models can facilitate the development of predictive tools and interventions customized to the specific needs of each patient.^9,10

Despite increasing interest in this topic, a comprehensive understanding of the global research landscape remains lacking. Bibliometric analysis serves as an effective tool for investigating research trends, significant authors, collaborations, and emerging topics through a quantitative evaluation of scientific literature.¹¹ Bibliometric studies that analyze publication trends, citation networks, and keyword co-occurrence can yield insights and inform future research directions.¹²

Bibliometric analysis is advantageous as it provides a more profound comprehension of the topic and suggests new avenues for exploration. An extensive evaluation of GL through machine learning and bibliometric analysis has enabled us to present an overview of the themes, trends, and bibliometric features currently found in this literature collection, covering the ten-year period from 2014 to 2024. The findings of our study offer important insights into loneliness within the geriatric population and indicate potential directions for further investigation utilizing machine learning methodologies in this rapidly developing field through bibliometric analysis.

Methods

Search strategy

Among the most extensive global databases of scientific literature, Scopus, we looked for relevant articles. Publications such as peer-reviewed journals, conference proceedings, commercial magazines, and book series are among the over 5,000 titles currently available on Scopus. Journal articles, conference proceedings, patents, and other publications covering any and all fields make up its approximately 75 million records. To find relevant documents, we used the following keywords: “artificial intelligence” OR “machine intelligence” OR “robot*” OR “robot technology” OR “assistant robot” OR “robot-assisted” OR “computational intelligence” OR “computer reasoning” OR “deep learning” OR “computer vision system” OR “sentiment analysis” OR “machine learning” OR “neural network*” OR “data learning” OR “expert* system*” OR “natural language processing” OR “support vector machine*” OR “decision tree*” OR “data mining” OR “deep learning” OR “neural network*” OR “bayesian network*” OR “intelligent learning” OR “feature* learning” OR “feature* extraction” OR “feature* mining” OR “feature* selection” OR “unsupervised clustering” OR “image* segmentation” OR “supervised learning” OR “semantic segmentation” OR “deep network*” OR “neural learning” OR “neural nets model” OR “graph mining” OR “data clustering” OR “big data” OR “knowledge graph”)) AND ALL ((solitude OR aloneness OR lonesomeness)) AND ALL ((“senior∗” OR “aged∗” OR “elder∗” OR “geri∗” OR “age∗” OR “older adult∗” OR “geriatric∗” OR “aging” OR “very elderly” OR “frail elderly”. After a thorough examination of every title of the retrieved articles, two experts who possess a minimum of five years of experience in conducting systematic reviews evaluated and gave their approval to our search strategy. They made their selection from among all of the publications that were pertinent, and any disagreements were settled by consulting with a third author. Finally, all of the retrieved articles were saved.

Screening

Only articles that utilized machine learning as a result and focused on generative learning were incorporated into the bibliometric analysis. No language restrictions were imposed. Letters, editorials, conference abstracts, and book chapters were excluded from consideration. In contrast, research papers and reviews published in peer-reviewed journals were included in the selection process. Due to the focus on the ten-year trend, publications issued before 2014 were excluded from consideration. Two experts in bibliometric analysis, possessing extensive experience, assessed the acquired articles in accordance with established standards and guidelines.¹³

Bibliometric analysis

To produce consistent and measurable data pertinent to policy management, bibliometric analysis¹⁴ serves as a quantitative assessment of scientific research, evaluating and examining prevailing research trends within a specific domain.¹⁵ Bibliometric analysis offers a comprehensive overview of a research subject and identifies potential areas for further investigation, along with the methodologies that authors have employed to achieve their objectives.¹⁶ Displaying the complete field will facilitate readers’ comprehension of GL research patterns and trends.

The publication patterns of the leading ten publications, institutes, authors, and countries were analyzed. The categories, percentages, and frequencies for each journal were presented. Ratios, frequencies, and percentages of related authors were computed for each country, considering both single and multiple countries. The frequency and citation rates for each author and institution were provided as well. Countries, journals, institutions, and authors were ranked based on their publication volume and research impact. Data were constructed and visualized utilizing VOSviewer (Leiden University) and Bibliometrix (an R package).

Results

Publication output

A total of 267 items were identified through the electronic database search. After screening, 112 individuals were excluded. The comprehensive bibliometric analysis comprised 155 publications. Figure 1 illustrates the growth in the number of GL articles over the past decade.

Figure 1. The distribution of articles by year between 2014 and 2024.

Table 1 presents the geographical distribution of the ten leading countries in GL research publications. The United States produced the highest number of publications (18), followed by China (14) and Canada (9). China exhibited the highest multiple country publication (MCP) ratio at 0.5, followed by Canada at 0.444 and the United Kingdom at 0.429. Figure 2 illustrates the countries that are linked through at least one research collaboration.

Table 1. Top 10 countries that published in 2014–2024.

Rank	Country	Articles	SCP	MCP	Freq	MCP ratio
1	USA	18	14	4	0.116	0.222
2	China	14	7	7	0.09	0.5
3	Canada	9	5	4	0.058	0.444
4	United Kingdom	7	4	3	0.045	0.429
5	Switzerland	6	3	3	0.039	0.5
6	Australia	5	4	1	0.032	0.2
7	France	5	4	1	0.032	0.2
8	Austria	4	4	0	0.026	0
9	Italy	4	4	0	0.026	0
10	Netherlands	4	3	1	0.026	0.25

Figure 2. The co-authorship network of country that contributed to the topic during 2014 and 2024.

Table 2 displays the ten leading institutes in GL research productivity. The University of California and the University of Pennsylvania each published seven articles, leading all universities, while the University of Copenhagen and the University of Zurich each published six articles.

Table 2. Top 10 institutions that published mangrove conservation papers 2014–2024.

Rank	Affiliation	Country	Articles
1	University of California	USA	7
2	University of Pennsylvania	USA	7
3	University of Copenhagen	Denmark	6
4	University of Zurich	Switzerland	6
5	Osaka University	Japan	5
6	University of Tsukuba	Japan	5
7	Tampere University	Finland	4
8	University of Maryland	USA	4
9	University of Rhode Island	USA	4
10	University of Toronto	USA	4

In order to conduct a co-authorship study and offer a thorough perspective of the entirety of the nation network in GL research, the VOSviewer tool was employed. The relationship between two nations is determined by the total number of publications that they have jointly authored. The study of authorship by nation produced a single cluster, which consisted of fifteen authors who are related to one another in that they have each published at least one paper. These categories are shown in Figure 3.

Figure 3. The co-authorship network of authors who contributed to the topic, 2014–2024.

When it comes to the subject of GL research, 267 publications were published by 640 different authors. The most prominent ten GL research authors are enumerated in Table 3. Three papers were published by Rigaud AS, and three papers were published by Pino M, who were the leading authors.

Table 3. Top 10 authors that published in 2014–2024.

Rank	Authors	Articles
1	Pino M.	3
2	Rigaud A.S.	3
3	Bayor A.A.	2
4	Brereton M.	2
5	Campo E.	2
6	Friedel E.	2
7	Heinz A.	2
8	Isabet B.	2
9	Kislev E.	2
10	Liu Y.	2

Co-occurrence analysis of top 100 keywords

The 267 articles consisted of five distinct keyword clusters. Table 4 presents the ten most frequently used keywords in the retrieved articles. Human, ageing, male, female, and loneliness were significant categories identified. The co-occurrence analysis of the top 100 phrases revealed the research hotspots in GL ( Figure 4).

Table 4. Top 10 author keywords 2014–2024.

Rank	Keywords	Occurrences
1	Human	76
2	Aged	64
3	Male	63
4	Female	62
5	Humans	59
6	Loneliness	50
7	Article	44
8	Adult	36
9	Social isolation	36
10	Machine learning	21

Figure 4. The co-occurrence network of the top 100 keywords in this research, 2014–2024.

Reference co-citation analysis

We conducted an in-depth examination of the knowledge base within the GL research domain. Table 5 presents the ten most highly cited articles. As of June 1, 2024 Arbabshirani, Plis,¹⁷ had the highest citation count, totalling 602. Published in NeuroImage in 2017. Figure 5 illustrates the three-field plot.

Table 5. Top 10 high cited articles 2014–2024.

Rank	First author/journal	DOI	Total citations	TC/Year
1	Arbabshirani, Plis,¹⁷ Neuroimage	10.1016/j.neuroimage.2016.02.079	602	75.25
2	Lim, Gleeson,²⁶ Soc Psychiatry Psychiatr Epidemiol	10.1007/s00127-020-01889-7	185	37.00
3	Schriber and Guyer,²⁷ Dev Cognitive Neurosci	10.1016/j.dcn.2015.12.009	163	18.11
4	Tsai, Shillair and Cotten,²⁸ 2017, J Appl Gerontol	10.1177/0733464815609440	157	19.63
5	Landeiro, Barrows,²⁹ Bmj Open	10.1136/bmjopen-2016-013778	136	17.00
6	Shackman, Tromp,³⁰ Psychol Bull	10.1037/bul0000073	123	13.67
7	Tomova, Wang,³¹ Nat Neurosci	10.1038/s41593-020-00742-z	111	22.20
8	Mateos-Pérez, Dadar,³² Neuroimage Clin	10.1016/j.nicl.2018.08.019	110	15.71
9	Waytz and Gray,³³ Perspect Psychol Sci	10.1177/1745691617746509	95	13.57
10	Hopkins and McKay,³⁴ Technol Forecast Soc Change	10.1016/j.techfore.2018.07.032	73	12.17

Figure 5. Three-Field plot of publications, 2014–2024.

Discussion

To the best of our knowledge, this is the inaugural bibliometric investigation examining GL through a machine learning methodology. This bibliometric analysis demonstrated a growing worldwide scholarly interest in utilising machine learning technologies to combat loneliness among the elderly. The persistent increase in publications over the past decade underscores the significance of this issue as a significant public health concern, as well as the potential for machine learning methodologies to provide valuable insights and solutions.^1,7

This study identifies the United States, China, and Canada as leading countries due to their robust research infrastructure, funding opportunities, and ageing populations.^18,19 Countries such as the United Kingdom, Switzerland, and Australia significantly contributed to the study output, indicating a heightened global awareness and commitment to addressing loneliness in the elderly population.

This study identifies key institutions and authors as the principal drivers of research in this discipline. Collaborations among these institutions and researchers facilitate information sharing, resource pooling, and the development of interdisciplinary approaches to address the complex issue of loneliness in the elderly.²⁰

Co-occurrence analysis revealed several emerging topics and trends, such as social media, depression, and artificial intelligence.^{21, 22, 23} The findings highlight the relationship between loneliness and mental health disorders, along with the potential of machine learning methods for creating predictive tools and personalised therapies.

Identifying prominent authors and their research topics may facilitate future collaborations and the dissemination of information. Expanding the existing body of research and leveraging the expertise of these professionals may facilitate the exploration of new strategies to mitigate loneliness among the elderly.^24,25

This bibliometric study provides valuable insights, yet it is important to acknowledge its significant limitations. Relying exclusively on the Scopus database may result in the exclusion of pertinent articles from various sources. The precision of the metadata associated with the articles imposes constraints on the analysis. Thirdly, bibliometric analysis prioritises quantitative evaluation over qualitative assessment of study findings and methodologies. The study does not account for potential biases or limitations in the machine learning techniques utilised in the analysed papers. Previous or contemporary developments may be excluded from the 2014–2024 time frame. In addition to these limitations, the findings underscore the growing importance of employing machine learning to tackle loneliness among the elderly, potentially guiding future treatment strategies and research initiatives.

Conclusion

In conclusion, this study highlights the increasing global focus on utilising machine learning to address the problem of loneliness in the elderly population. This bibliometric analysis emphasises key authors, collaborative efforts, and emerging themes such as social media, depression, and predictive modelling. It also indicates increasing trends in studies, primarily from the US, China, and Canada. These findings can guide future studies, collaborative efforts, and the creation of effective treatments focused on improving the quality of life for the elderly. This study underscores the significance of bibliometric analysis in tackling this crucial public health concern.

Ethics approval

No ethical approval and consent were required for the current study.

Data availability statement

All datasets supporting this article are accessible via the following link: https://doi.org/10.5281/zenodo.20093647.³⁵

Data are available under the terms of the Creative Commons Attribution 4.0 International.

Acknowledgements

None.

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Version 1

VERSION 1 PUBLISHED 26 May 2026

Author details Author details

¹ Faculty of Nursing, Chulalongkorn University, Bangkok, Bangkok, Thailand
² Nurse Professional Education Study Program, Faculty of Medicine, Universitas Halu Oleo, Kendari, South East Sulawesi, Indonesia
³ School of Nursing, College of Nursing, Taipei Medical University, Taipei City, Taipei City, Taiwan
⁴ Department of Nursing, Wan Fang Hospital, Taipei Medical University, Taipei City, Taipei City, Taiwan
⁵ Research Center in Nursing Clinical Practice, Wan Fang Hospital, Taipei Medical University, Taipei City, Taipei City, Taiwan
⁶ Research Center of Sleep Medicine, College of Medicine, Taipei Medical University, Taipei City, Taipei City, Taiwan
⁷ Department of Nursing, Taipei Medical University Hospital, Taipei City, Taipei City, Taiwan
⁸ Research Center of Sleep Medicine, Taipei Medical University Hospital, Taipei City, Taipei City, Taiwan

Sri Susanty
Roles: Conceptualization, Data Curation, Formal Analysis, Funding Acquisition, Investigation, Methodology, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

Yeu-Hui Chuang
Roles: Supervision, Validation, Writing – Original Draft Preparation, Writing – Review & Editing

Hsiao-Yean Chiu
Roles: Supervision, Validation, Writing – Original Draft Preparation, Writing – Review & Editing

Faizul Hasan
Roles: Conceptualization, Data Curation, Formal Analysis, Funding Acquisition, Investigation, Methodology, Project Administration, Resources, Software, Supervision, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

Competing interests

No competing interests were disclosed.

Grant information

This research was supported by the Second Century Fund (C2F), Chulalongkorn University, Bangkok, Thailand. Funding recipient: Sri Susanty.
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Susanty S, Chuang YH, Chiu HY and Hasan F. A Machine Learning–Based Bibliometric Analysis of Global Research Trends on Loneliness in Older Adults [version 1; peer review: awaiting peer review]. F1000Research 2026, 15:798 (https://doi.org/10.12688/f1000research.182051.1)

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[1] 1. Cacioppo JT, Cacioppo S: The growing problem of loneliness. The Lancet. 2018; 391(10119): 426. PubMed Abstract | Publisher Full Text | Free Full Text

[2] 2. Courtin E, Knapp M: Social isolation, loneliness and health in old age: a scoping review. Health Soc Care Community. 2017; 25(3): 799–812. PubMed Abstract | Publisher Full Text

[3] 3. Hawkley LC, Cacioppo JT: Loneliness matters: a theoretical and empirical review of consequences and mechanisms. Ann Behav Med. 2010; 40(2): 218–227. PubMed Abstract | Publisher Full Text | Free Full Text

[4] 4. Kuiper JS, Zuidersma M, Oude Voshaar RC, et al.: Social relationships and risk of dementia: A systematic review and meta-analysis of longitudinal cohort studies. Ageing Res Rev. 2015; 22: 39–57. PubMed Abstract | Publisher Full Text

[5] 5. Lara E, Martín-María N, de la Torre-Luque A, et al.: Does loneliness contribute to mild cognitive impairment and dementia? A systematic review and meta-analysis of longitudinal studies. Ageing Res Rev. 2019; 52: 7–16. PubMed Abstract | Publisher Full Text

[6] 6. Valtorta NK, Kanaan M, Gilbody S, et al.: Loneliness and social isolation as risk factors for coronary heart disease and stroke: systematic review and meta-analysis of longitudinal observational studies. Heart. 2016; 102(13): 1009–1016. PubMed Abstract | Publisher Full Text | Free Full Text

[7] 7. Bello-Valle AS, Martínez-Rebollar A, Sánchez W, et al.: A predictive model for automatic detection of loneliness and social isolation using machine learning. Comput Sist. 2022; 26(1): 113–124. Publisher Full Text

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A Machine Learning–Based Bibliometric Analysis of Global Research Trends on Loneliness in Older Adults

Abstract

Background

Methods

Results

Conclusions

Keywords

Introduction

Methods

Search strategy

Screening

Bibliometric analysis

Results

Publication output

Figure 1. The distribution of articles by year between 2014 and 2024.

Table 1. Top 10 countries that published in 2014–2024.

Figure 2. The co-authorship network of country that contributed to the topic during 2014 and 2024.

Table 2. Top 10 institutions that published mangrove conservation papers 2014–2024.

Figure 3. The co-authorship network of authors who contributed to the topic, 2014–2024.

Table 3. Top 10 authors that published in 2014–2024.

Co-occurrence analysis of top 100 keywords

Table 4. Top 10 author keywords 2014–2024.

Figure 4. The co-occurrence network of the top 100 keywords in this research, 2014–2024.

Reference co-citation analysis

Table 5. Top 10 high cited articles 2014–2024.

Figure 5. Three-Field plot of publications, 2014–2024.

Discussion

Conclusion

Ethics approval

Data availability statement

Acknowledgements

References

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