Artificial Intelligence in Library and Information Science: Co-citation Analysis, Word Co-occurrence, and Topic Trends

Document Type : Research Paper

Authors

1 Professor, Department of Information Science and Knowledge Studies, Faculty of Psychology and Education, Kharazmi University, Tehran, Iran,

2 Ph.D in Information Science and Knowledge Studies, Kharazmi University, Tehran, Iran.

3 Assistant Professor, Department of Information Science and Knowledge Studies, Faculty of Psychology and Education, Kharazmi University, Tehran, Iran.

Abstract

Purpose: Artificial intelligence (AI) has profoundly impacted the field of Library and Information Science (LIS) by transforming traditional systems of knowledge management, information services, and user interactions. As AI technologies rapidly evolve—from machine learning and natural language processing (NLP) to large language models (LLMs) and generative AI—understanding the intellectual landscape of AI research in LIS becomes increasingly crucial. This study aims to identify, map, and analyze the thematic and citation structures of AI-related publications in LIS to uncover dominant research clusters, emerging trends, and potential gaps.
Methodology: This applied scientometric study utilized a mixed-methods approach combining co-citation and keyword co-occurrence analyses. A dataset of 3,066 records published between 2005 and 2024 was retrieved from the Web of Science Core Collection using the query: (WC=Library and Information Science) AND (TS=AI OR Artificial Intelligence). Co-citation networks were constructed using CiteSpace software across four five-year intervals, applying a Top N = 50 threshold. Cluster quality was evaluated using Modularity (Q) and Silhouette (S) indices. Cluster labels were generated through software algorithms, manual document inspection, and expert consensus involving five specialists in AI and Library and Information Science (LIS). Burst detection was employed to identify influential articles. Keyword co-occurrence networks were generated via VOSviewer with a controlled vocabulary. Hot topic trends and keyword trajectories were visualized using Biblioshiny from the Bibliometrix R package.
Findings: The co-citation analysis revealed 24 thematic clusters, with five key clusters including algorithmic decision-making (2016), the moderating role of AI (2019), explainable AI (2019), systematic AI reviews (2020), and AI applications in public services (2020). A review of highly cited articles indicated that Dwivedi et al. (2021), whose article explored multidisciplinary perspectives on AI, had the greatest impact with 133 citations. They were followed by Duan et al. (2019), whose study focused on AI's role in decision-making, with 107 citations, and Sun & Medaglia (2019), who examined AI applications in the public sector, with 75 citations. The burstiness index indicated that Russell & Norvig (2016), authors of the influential textbook "Artificial Intelligence: A Modern Approach", showed the most significant growth in impact. The keyword co-occurrence map identified five key clusters: information systems and knowledge management; technology adoption and user interaction; machine learning and AI, language models and information services; and AI ethics and governance. These clusters indicate research trends focused on data management, user behavior analysis, model development, information service improvement, and ethical challenges. Historical trend analysis reveals a shift from traditional concepts such as ontology and digital libraries toward machine learning, natural language processing (NLP), and large language models (LLMs). Currently, research primarily emphasizes generative AI, transparency, and governance. Additionally, highly cited terms in this domain include Industry 4.0, decision-making, and big data analytics, underscoring AI’s role in improving human interactions and data management. Furthermore, trend analysis indicates that AI research within library and information science (LIS) has evolved from classical concepts to advanced technologies such as machine learning and neural networks. The focus has shifted from data mining and information retrieval to deep learning and NLP. Recently, large language models and generative AI have driven fundamental transformations in this field. Additionally, ethical concerns and algorithmic transparency have become increasingly important. This trend reflects a transition toward smarter, more automated, and language-processing-oriented systems, with heightened attention to ethical considerations.
Conclusion: The findings indicate that research on AI within LIS is primarily focuses on five key areas: data management, technology adoption, machine learning, language models, and ethical issues. The interconnections among these clusters suggest that the successful implementation of AI requires effective coordination between data management, user behavior analysis, machine learning model development, and ethical compliance. These insights can assist policymakers, researchers, and professionals in optimizing the use of AI technologies within information systems. Furthermore, recent developments highlight the emergence of AI literacy and the integration of university libraries with modern technologies. Emphasizing user acceptance, trust in AI systems, and their impact on information policies suggests that AI’s role in LIS extends beyond technical tools to encompass social, ethical, and policy dimensions. This trend illustrates a shift from classical topics toward intelligent interactions, big data analytics, and the adoption of emerging technologies, emphasizing the need for further research on AI governance, transparency, and social implications. Overall, this study helps identify both strong and weak research areas in the field, highlighting the necessary capacities to enhance research and promote responsible AI development in LIS. Ultimately, scientometric analysis can aid policymakers and planners in optimizing resource allocation, improving socio-economic structures, and advancing sustainable AI development.

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References

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