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 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 Abstract Purpose: The study aims to analyze the synergy of Artificial Intelligence (AI), with scientometrics, webometrics, and bibliometrics to unlock and to emphasize the potential of the applications and benefits of AI algorithms in these fields. Design/methodology/approach: By conducting a systematic literature review, our aim is to explore the potential of AI in revolutionizing the methods used to measure and analyze scholarly communication, identify emerging research trends, and evaluate the impact of scientific publications. To achieve this, we implemented a comprehensive search strategy across reputable databases such as ProQuest, IEEE Explore, EBSCO, Web of Science, and Scopus. Our search encompassed articles published from January 1, 2000, to September 2022, resulting in a thorough review of 61 relevant articles. Findings: (i) Regarding scientometrics, the application of AI yields various distinct advantages, such as conducting analyses of publications, citations, research impact prediction, collaboration, research trend analysis, and knowledge mapping, in a more objective and reliable framework. (ii) In terms of webometrics, AI algorithms are able to enhance web crawling and data collection, web link analysis, web content analysis, social media analysis, web impact analysis, and recommender systems. (iii) Moreover, automation of data collection, analysis of citations, disambiguation of authors, analysis of co-authorship networks, assessment of research impact, text mining, and recommender systems are considered as the potential of AI integration in the field of bibliometrics. Originality/value: This study covers the particularly new benefits and potential of AI-enhanced scientometrics, webometrics, and bibliometrics to highlight the significant prospects of the synergy of this integration through AI Introduction Artificial Intelligence (AI) has revolutionized various fields, in particular scientometrics, webometrics, and bibliometrics [1, 2]. Scientometrics is a field that involves the quantitative analysis of scientific literature to measure various aspects of scientific research, such as productivity, impact, and collaboration patterns [3]. It uses bibliographic data and citation analysis to understand the dynamics of scientific knowledge production and dissemination [4]. Webometrics, on the other hand, focuses on the quantitative analysis of web-based information, particularly websites and hyperlinks, to assess the impact and visibility of individuals, organizations, or research institutions on the web [5]. It employs web crawling and link analysis techniques to examine web-based structures and interactions [6]. Bibliometrics is a field that applies mathematical and statistical methods to analyze patterns of publication, citation, and collaboration in academic literature [7]. It measures the impact and influence of scholarly publications, authors, and institutions based on citation analysis and other bibliographic data [8]. These three fields are closely related to each other as they all involve the quantitative analysis of information and aim to provide insights into the production, dissemination, and impact of scientific knowledge. They share common methodologies and techniques, such as data mining, network analysis, and statistical modeling. In the following, we demonstrate prospects based on previous applications. Furthermore, we conclude that we provide also ground for further research and prospective innovation in the field of informetrics, ultimately leading to more accurate, efficient, and insightful analyses in evidence-based decision-making. Researchers face a challenge when dealing with the availability of vast amounts of scholarly publications, as it becomes difficult to extract knowledge, improve data analysis, and make wellinformed decisions. AI-enhanced algorithms and techniques have played a crucial role in automating the identification, classification, and analysis of scientific literature [9]. Moreover, the application of AI algorithms has opened up new possibilities, enabling efficient data processing, pattern recognition, and knowledge extraction [10, 11]. Thus, by harnessing the power of AI, researchers can now delve into large-scale publication metrics, identify research trends, and track the influence and impact of scientific productions [10, 12, 13]. First, by leveraging natural language processing (NLP) algorithms, machine learning techniques, and deep learning approaches, AI can extract key information from scientific papers from a scientometric perspective to gain a comprehensive understanding of research trends, collaborations, and impact within specific domains [14]. Next, in terms of webometrics, AI algorithms can collect data from various online sources through web scraping, including web pages, blogs, forums, and social media posts. Machine learning, data mining algorithms, and deep learning (DL) techniques can extract data and patterns to help researchers understand and predict online users’ behaviors, and digital impact [15, 16]. “Finally”, through AI-powered algorithms, bibliometricians can analyze large-scale bibliographic and citation databases, such as Web of Science or Scopus, to uncover patterns, trends, and relationships among scientific productions [17]. These algorithms and approaches are helpful for policymakers and academicians to assess the impact of researchers, institutions, or scientific fields, facilitating evidence-based decisions, policy making, innovation mapping, and forecasting future-oriented developments [18]. While AI has shown great promise in improving the efficiency and accuracy of scientometric, webometric, and bibliometric analyses, there remains a lack of comprehensive understanding of the cutting-edge techniques and advancements in this rapidly evolving field. As researchers strive to harness the power of AI to gain deeper insights into scholarly communication patterns, citation networks, and the impact of research, it is crucial to conduct a systematic review that consolidates and synthesizes the latest developments and methodologies. Therefore, the problem at hand is the absence of a comprehensive overview and analysis of the current state-of-the-art AI-enhanced techniques in scientometrics, webometrics, and bibliometrics. This knowledge gap inhibits researchers and practitioners from fully capitalizing on the potential benefits and advancements offered by AI in these domains. By conducting a systematic review, we aim to address this gap and provide a comprehensive understanding of the state-of-the-art AI techniques, their applications, and their impact on the field of informetrics. In our study, we focus on these three specific fields (scientometrics, webometrics, and bibliometrics) because they represent key areas where the application of artificial intelligence (AI) has had a significant impact. AI techniques, such as machine learning and natural language processing, have greatly enhanced the analysis of large-scale bibliographic and web-based data, enabling more accurate and efficient measurement of scientific impact, knowledge diffusion, and web visibility. Through this systematic review, we seek to shed light on the potential of AI to transform the way we measure and analyze scholarly communication, identify emerging research trends, and assess the impact of scientific publications. By doing so, we hope to inspire further research and innovation in the field of informetrics, ultimately leading to more accurate, efficient, and insightful analyses that can drive scientific progress and informed evidence-based decision-making. This paper is available on arxiv under CC BY 4.0 DEED license. 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. Authors: 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 Abstract and Introduction Abstract and Introduction Materials and Methods Materials and Methods Results Results RQ 1: AI and scientometrics RQ 1: AI and scientometrics RQ 2: AI and webometrics RQ 2: AI and webometrics RQ 3: AI and bibliometrics RQ 3: AI and bibliometrics Discussion Discussion RQ 4: Future of Scientometrics, Webometrics, and Bibliometrics with AI RQ 4: Future of Scientometrics, Webometrics, and Bibliometrics with AI RQ 5: Ethical Considerations of Scientometrics, Webometrics, and Bibliometrics with AI RQ 5: Ethical Considerations of Scientometrics, Webometrics, and Bibliometrics with AI Conclusion, Limitations, and References Conclusion, Limitations, and References Abstract Purpose : The study aims to analyze the synergy of Artificial Intelligence (AI), with scientometrics, webometrics, and bibliometrics to unlock and to emphasize the potential of the applications and benefits of AI algorithms in these fields. Purpose Design/methodology/approach: By conducting a systematic literature review, our aim is to explore the potential of AI in revolutionizing the methods used to measure and analyze scholarly communication, identify emerging research trends, and evaluate the impact of scientific publications. To achieve this, we implemented a comprehensive search strategy across reputable databases such as ProQuest, IEEE Explore, EBSCO, Web of Science, and Scopus. Our search encompassed articles published from January 1, 2000, to September 2022, resulting in a thorough review of 61 relevant articles. Design/methodology/approach: Findings : (i) Regarding scientometrics, the application of AI yields various distinct advantages, such as conducting analyses of publications, citations, research impact prediction, collaboration, research trend analysis, and knowledge mapping, in a more objective and reliable framework. (ii) In terms of webometrics, AI algorithms are able to enhance web crawling and data collection, web link analysis, web content analysis, social media analysis, web impact analysis, and recommender systems. (iii) Moreover, automation of data collection, analysis of citations, disambiguation of authors, analysis of co-authorship networks, assessment of research impact, text mining, and recommender systems are considered as the potential of AI integration in the field of bibliometrics. Findings (i) (ii) (iii) Originality/value: This study covers the particularly new benefits and potential of AI-enhanced scientometrics, webometrics, and bibliometrics to highlight the significant prospects of the synergy of this integration through AI Originality/value: Introduction Artificial Intelligence (AI) has revolutionized various fields, in particular scientometrics, webometrics, and bibliometrics [1, 2]. Scientometrics is a field that involves the quantitative analysis of scientific literature to measure various aspects of scientific research, such as productivity, impact, and collaboration patterns [3]. It uses bibliographic data and citation analysis to understand the dynamics of scientific knowledge production and dissemination [4]. Webometrics, on the other hand, focuses on the quantitative analysis of web-based information, particularly websites and hyperlinks, to assess the impact and visibility of individuals, organizations, or research institutions on the web [5]. It employs web crawling and link analysis techniques to examine web-based structures and interactions [6]. Bibliometrics is a field that applies mathematical and statistical methods to analyze patterns of publication, citation, and collaboration in academic literature [7]. It measures the impact and influence of scholarly publications, authors, and institutions based on citation analysis and other bibliographic data [8]. These three fields are closely related to each other as they all involve the quantitative analysis of information and aim to provide insights into the production, dissemination, and impact of scientific knowledge. They share common methodologies and techniques, such as data mining, network analysis, and statistical modeling. In the following, we demonstrate prospects based on previous applications. Furthermore, we conclude that we provide also ground for further research and prospective innovation in the field of informetrics, ultimately leading to more accurate, efficient, and insightful analyses in evidence-based decision-making. Researchers face a challenge when dealing with the availability of vast amounts of scholarly publications, as it becomes difficult to extract knowledge, improve data analysis, and make wellinformed decisions. AI-enhanced algorithms and techniques have played a crucial role in automating the identification, classification, and analysis of scientific literature [9]. Moreover, the application of AI algorithms has opened up new possibilities, enabling efficient data processing, pattern recognition, and knowledge extraction [10, 11]. Thus, by harnessing the power of AI, researchers can now delve into large-scale publication metrics, identify research trends, and track the influence and impact of scientific productions [10, 12, 13]. First, by leveraging natural language processing (NLP) algorithms, machine learning techniques, and deep learning approaches, AI can extract key information from scientific papers from a scientometric perspective to gain a comprehensive understanding of research trends, collaborations, and impact within specific domains [14]. Next, in terms of webometrics, AI algorithms can collect data from various online sources through web scraping, including web pages, blogs, forums, and social media posts. Machine learning, data mining algorithms, and deep learning (DL) techniques can extract data and patterns to help researchers understand and predict online users’ behaviors, and digital impact [15, 16]. “Finally”, through AI-powered algorithms, bibliometricians can analyze large-scale bibliographic and citation databases, such as Web of Science or Scopus, to uncover patterns, trends, and relationships among scientific productions [17]. These algorithms and approaches are helpful for policymakers and academicians to assess the impact of researchers, institutions, or scientific fields, facilitating evidence-based decisions, policy making, innovation mapping, and forecasting future-oriented developments [18]. While AI has shown great promise in improving the efficiency and accuracy of scientometric, webometric, and bibliometric analyses, there remains a lack of comprehensive understanding of the cutting-edge techniques and advancements in this rapidly evolving field. As researchers strive to harness the power of AI to gain deeper insights into scholarly communication patterns, citation networks, and the impact of research, it is crucial to conduct a systematic review that consolidates and synthesizes the latest developments and methodologies. Therefore, the problem at hand is the absence of a comprehensive overview and analysis of the current state-of-the-art AI-enhanced techniques in scientometrics, webometrics, and bibliometrics. This knowledge gap inhibits researchers and practitioners from fully capitalizing on the potential benefits and advancements offered by AI in these domains. By conducting a systematic review, we aim to address this gap and provide a comprehensive understanding of the state-of-the-art AI techniques, their applications, and their impact on the field of informetrics. In our study, we focus on these three specific fields (scientometrics, webometrics, and bibliometrics) because they represent key areas where the application of artificial intelligence (AI) has had a significant impact. AI techniques, such as machine learning and natural language processing, have greatly enhanced the analysis of large-scale bibliographic and web-based data, enabling more accurate and efficient measurement of scientific impact, knowledge diffusion, and web visibility. Through this systematic review, we seek to shed light on the potential of AI to transform the way we measure and analyze scholarly communication, identify emerging research trends, and assess the impact of scientific publications. By doing so, we hope to inspire further research and innovation in the field of informetrics, ultimately leading to more accurate, efficient, and insightful analyses that can drive scientific progress and informed evidence-based decision-making. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

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