AI Anxiety: A Web of Science-Based Bibliometric Analysis

Abstract

Aim: Artificial Intelligence Anxiety (AI Anxiety) refers to the apprehension and distrust individuals may feel in response to the rapid development and integration of artificial intelligence into various aspects of life. These emotions are often driven by concerns about AI's potential implications in domains such as employment, security, privacy, and human interaction. This study aims to conduct a comprehensive bibliometric analysis of the scientific literature on 'AI Anxiety' published between 2011 and 2024. Methods: A total of 80 articles indexed in the Web of Science (WoS) Core Collection database were analysed. The study evaluated parameters including the most cited publications, annual distribution of research, contributing countries, leading publishers, main research domains, and keyword trends. Network analyses involving coauthorship, author citations, institutional citations, and country-level citations were conducted using VOSviewer software. Results: The findings indicate a notable increase in AI Anxiety-related studies, particularly between 2021 and 2024. Highly cited articles include works by Youn (2021) and Wang (2022). The United States emerged as the leading contributor, followed by China and T & uuml;rkiye. Prominent publishers were Elsevier, Springer Nature, and Taylor & Francis. In the coauthorship network, authors such as Merlo and Johnson occupied central positions. Frequently used keywords included 'Artificial Intelligence', 'AI Anxiety', 'Technology Acceptance Model', and 'Trust'. Country citation analysis revealed that the United States and China occupied central roles with strong citation linkages to other countries. Conclusion: The study highlights the growing scholarly interest in the psychological and societal implications of artificial intelligence. It also provides a roadmap for future research directions by identifying key contributors, collaborative patterns, and thematic trends within the field of AI Anxiety.