Authors:
(1) Hamid Reza Saeidnia, Department of Information Science and Knowledge Studies, Tarbiat Modares University, Tehran, Islamic Republic of Iran;
(2) Elaheh Hosseini, Department of Information Science and Knowledge Studies, Faculty of Psychology and Educational Sciences, Alzahra University, Tehran, Islamic Republic of Iran;
(3) Shadi Abdoli, Department of Information Science, Université de Montreal, Montreal, Canada
(4) Marcel Ausloos, School of Business, University of Leicester, Leicester, UK and Bucharest University of Economic Studies, Bucharest, Romania.
Table of Links
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 2: AI and webometrics
In webometrics, AI can provide several specific benefits including Web Crawling and Data Collection, Web Link Analysis, Web Content Analysis, Social Media Analysis, Web Impact Analysis, and Recommender Systems as sketched in Figure 3, and e.g. it has been demonstrated through papers like [9, 10, 21, 36-45].
These 6 considerations point to the potential benefits and suggest focused strategies for utilizing AI capabilities in webometrics. The resulting findings highlight how AI can improve the quality, accessibility, and data collection processes in webometrics analyses, as outlined in Table 2.
Indeed, algorithms based on artificial intelligence can automatically crawl and collect data from websites, including institutional websites, scientific research portals, and online repositories [39, 42]. This enables researchers to gather large amounts of web-based information for analysis, including publication data, author profiles, and citation patterns.
In order to understand the relationship between publications, websites, and authors, artificial intelligence approaches can analyze hyperlink structures and web link patterns [9, 43]. By analyzing the link structure, AI algorithms can identify influential websites and authors, as well as detect communities, collaborations, and research networks within the web-based scientific ecosystem [17].
AI techniques, such as natural language processing and machine learning, can be employed to analyze the content of webpages and scientific publications available online [40, 41]. This enables researchers to extract key information, such as keywords, topics, and sentiments, from web-based documents, facilitating comprehensive analysis and understanding of research outputs.
AI can analyze social media platforms, such as Twitter, to understand the online discussions, trends, and interactions related to scientific research [36, 38, 44]. By analyzing hashtags, mentions, and user behavior, AI algorithms can identify influential research topics, key opinion leaders, and potential collaborations within the online scientific community, as demonstrated in such previous works.
AI can assess the impact and visibility of scientific research on the web [37, 46]. Indeed, by analyzing web traffic, page views, and social media metrics, AI algorithms can provide insights into the online visibility, dissemination, and engagement of scientific publications, authors, and research institutions.
“Finally”, AI-powered recommender systems can assist researchers in discovering relevant scientific websites, online resources, and research collaborations [35, 45]. These papers, based on user preferences, reading behavior, and web usage data, show that personalized recommendations can be generated using AI algorithms, making it easier for researchers to explore the web-based scientific landscape and discover new opportunities for further research.
This paper is available on arxiv under CC BY 4.0 DEED license.