"AI can enhance data collection, analysis, prediction, and evaluation processes"

Table of Links

Abstract and Introduction

Materials and Methods

Results

RQ 1: AI and scientometrics

RQ 2: AI and webometrics

RQ 3: AI and bibliometrics

Discussion

RQ 4: Future of Scientometrics, Webometrics, and Bibliometrics with AI

RQ 5: Ethical Considerations of Scientometrics, Webometrics, and Bibliometrics with AI

Conclusion, Limitations, and References

Conclusion

In this report, we emphasize the importance and potential of integrating AI algorithms with scientometrics, webometrics, and bibliometrics, through numerous examples in the literature. The paradigm shift undergone by AI algorithms in these fields has been shown to have revealed new possibilities for analysis, prediction, and pattern mining-based recommendations. Within this review, the paper contributes to underscoring the prominent prospects and value of integrating AI in scientometrics, webometrics, and bibliometrics, i.e., to signify the synergy that can be achieved and fostered through this integration.

In brief, AI helps scientometrics by providing efficient and accurate methods to analyze and derive insights from scientific publications, citation networks, and collaborative relationships. This should enable researchers to gain a deeper understanding of scientific knowledge, trends, and impact, facilitating better decision-making and advances in scientific research. Moreover, AI enhances webometrics by providing efficient and automated methods to analyze web-based scientific data, understand link structures and social interactions, assess web impact, and provide personalized recommendations. This enables researchers to gain insights into the web-based scientific ecosystem, facilitate collaborations, and improve research visibility and impact in the digital age. In addition, AI enhances the bibliometrics field of activities by automating data collection, providing accurate author disambiguation, analyzing citation networks, assessing research impact, and providing personalized recommendations. This enables researchers to gain insights into scholarly communication, assess research performance, and make informed decisions in their bibliometric analyses. Overall, AI presents an efficient and scalable approach to scientometrics, webometrics, and bibliometrics, enabling researchers to extract meaningful insights from vast and diverse sources of scientific information.

In conclusion, the integration of artificial intelligence (AI) into scientometrics, webometrics, and bibliometrics holds significant potential for advancing research and understanding in these fields. AI can enhance data collection, analysis, prediction, and evaluation processes, providing researchers with valuable insights and improving decision-making processes.

However, the use of AI in these areas also raises important ethical considerations that must be carefully addressed. Data privacy and security, bias, and fairness, transparency and explainability, accountability and responsibility, informed consent, impact on employment and society, and continuous monitoring and evaluation are among the key ethical considerations that should be taken into account. To ensure the responsible and ethical use of AI, interdisciplinary collaboration, stakeholder engagement, and ongoing evaluation are crucial. Researchers, policymakers, ethicists, and stakeholders from various fields should work together to develop guidelines, frameworks, and best practices that promote ethical AI use in scientometrics, webometrics, and bibliometrics. By addressing these ethical considerations, we can harness the full potential of AI to advance knowledge, improve research practices, and contribute to the betterment of society while ensuring fairness, transparency, and accountability in the use of these technologies.

Limitations

In this particular study, we did not include the gray literature in our search and review process, nor did we manually search in Google Scholar. Instead, our intention was to focus on searching in reliable databases. While Google Scholar is often referred to as a database, it is actually a search engine that may not include high-quality articles and may only retrieve reliable studies. By not searching in Google Scholar, we aimed to minimize the number of overlapping studies.

However, it is important to note that this highly technical approach may have resulted in overlooking certain articles, which could regretfully lead to our study excluding relevant information. We consider that up to the time of writing and submitting this paper, we safeguard against much omission. Yet, for future studies, it may be beneficial to conduct a comprehensive review that includes the gray literature, in order to provide readers with a broader perspective.

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