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

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

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

From the above, one can imagine if not research gaps, at least directions for further progress. Artificial intelligence (AI) has the potential to significantly benefit all three fields - scientometrics, webometrics, and bibliometrics. However, the extent to which AI can perform and its future implications may vary in each field.

It has been shown here above that AI can greatly enhance scientometrics by improving data collection and analysis, text mining and information retrieval, identification of emerging research trends, visualization techniques, research evaluation, and collaboration and networking. The use of AI algorithms can automate processes, increase efficiency, and provide deeper insights into scientific literature [21-31]. The future of scientometrics with AI is likely to involve more advanced AI algorithms, improved integration of various data sources, and increased automation, leading to more accurate and comprehensive analyses.

AI can play a significant role in webometrics by improving data collection and analysis, web link analysis, web content analysis, web impact assessment, web usage mining, and efficient web crawling and data extraction [9, 10, 21, 36-41, 43-45]. AI techniques can help extract valuable information from the web, analyze user behavior, and assess the impact of web resources [9, 36- 45]. The future of webometrics with AI may involve advancements in AI algorithms for web data analysis, better understanding of user behavior, and improved techniques for web impact assessment.

AI can enhance bibliometrics by improving publication analysis, citation analysis, author disambiguation, predictive models, collaboration analysis, and research evaluation. AI algorithms can automate processes, provide accurate citation analysis, and develop predictive models for future research trends [28-30, 47-53]. The future of bibliometrics with AI may involve more advanced techniques for author disambiguation, improved prediction models, integration of alternative metrics, and better evaluation of research impact beyond traditional citation counts.

In terms of which field AI can perform the most, it is difficult to determine a clear winner. AI has the potential to significantly benefit all three fields and can perform exceptionally well in each, depending on the specific applications and techniques employed. The effectiveness of AI in each field will also depend on the availability and quality of data, the complexity of the analysis required, and the specific research questions being addressed.

The future of these three areas with AI is promising. As AI technologies continue to advance, we can expect more sophisticated algorithms, improved integration of various data sources, and enhanced automation and efficiency in scientometrics, webometrics, and bibliometrics. The use of AI will likely lead to more accurate and comprehensive analyses, better understanding of research trends and impact, and improved decision-making processes in academia, research institutions, and funding agencies.