A hybrid human–AI tool for scientometric analysis

Correia, António; Grover, Andrea; Jameel, Shoaib; Schneider, Daniel; Antunes, Pedro; Fonseca, Benjamim

doi:10.1007/s10462-023-10548-7

Cited by 4 publications

(2 citation statements)

References 79 publications

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“…The scientometric analysis is used to map the research structure and evaluate the factual production quantitatively [11][12][13][14]. Analyzing publishing, citation, and cooperation patterns within an area of study or among scholars in general is the goal of this method [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

“…Measuring the influence of research publications and academic journals, comprehending scientific citations, and using these metrics in policy and management contexts are some of the major research challenges. The scientometric approach finds research subjects and creates a network model using bibliometric data [12,14]. To illustrate the intellectual perspective of a certain knowledge domain, scientometric analysis creates network models, which can help researchers achieve their study goals and find answers to their problems.…”

Section: Introductionmentioning

confidence: 99%

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Emerging Themes and Research Frontiers in Suicide Prediction: A Scientometric Analysis

Abraham,

K R,

Sony

et al. 2024

Cureus

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Suicide remains a critical global health issue despite advancements in mental health treatment. The purpose of this analysis is to emphasize the development, patterns, and noteworthy outcomes of suicide prediction research. It also helps to uncover gaps and areas of under-researched topics within suicide prediction. A scientometric analysis was conducted using Biblioshiny and VOSviewer. To thoroughly assess the academic literature on suicide prediction, various scientometric methodologies such as trend analysis and citation analysis were employed. We utilized the temporal features of the Web of Science to analyze publication trends over time. Author affiliation data were used to investigate the geographic distribution of research. Cluster analysis was performed by grouping related keywords into clusters to identify overarching themes within the literature. A total of 1,703 articles from 828 different sources, spanning from 1942 to 2023, were collected for the analysis. Machine learning techniques might have a big influence on suicide-related event prediction, which would enhance attempts at suicide prevention and intervention. The conceptual understanding of suicide prediction is enhanced by scientometric analysis, which further uncovers the research gap and literature in this area. Suicide prediction research underscores that suicidal behavior is not caused by a single factor but is the result of a complex interplay of multiple factors. These factors may include biological, psychological, social, and environmental factors. Understanding and integrating these factors into predictive models is a theoretical advancement in the field. Unlike previous bibliometric studies in the field of suicide prediction that have typically focused on specific subtopics or data sources, our analysis offers a comprehensive mapping of the entire landscape. We encompass a wide range of suicide prediction literature, including research from medical, psychological, and social science domains, thus providing a holistic overview.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Emerging Themes and Research Frontiers in Suicide Prediction: A Scientometric Analysis

Abraham,

K R,

Sony

et al. 2024

Cureus

View full text Add to dashboard Cite

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NLP-Crowdsourcing Hybrid Framework for Inter-Researcher Similarity Detection

Correia,

Guimarães,

Paredes

et al. 2023

IEEE Trans. Human-Mach. Syst.

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The Automation of Science? Possibilities and Boundaries of AI Applications for Conducting Systematic Literature Reviews

Tomczyk,

Brüggemann,

Doligalski

2024

Int. J. Artif. Intell. Tools

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Science automation, driven by advanced technologies, such as artificial intelligence (AI), is reshaping research in a variety of disciplines. The exponential growth of publications increases the need for automation in systematic literature reviews (SLRs). In this study, we investigated how AI applications support SLRs at different stages. After evaluating twenty-two AI applications, we identified the most useful based on their functionalities in the SLR process. This study provides important insights into the dynamic interplay between science automation, AI, and a systematic literature review. The main contribution is the identification and discussion of the relevance of the analyzed applications at different stages of the systematic literature review. We also discuss the potential for the automation and augmentation of research activities to address various scientific problems.

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A hybrid human–AI tool for scientometric analysis

Cited by 4 publications

References 79 publications

Emerging Themes and Research Frontiers in Suicide Prediction: A Scientometric Analysis

Emerging Themes and Research Frontiers in Suicide Prediction: A Scientometric Analysis

NLP-Crowdsourcing Hybrid Framework for Inter-Researcher Similarity Detection

The Automation of Science? Possibilities and Boundaries of AI Applications for Conducting Systematic Literature Reviews

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