Bibliometric Analysis of Human-Computer Interaction

Abstract

Human–Computer Interaction (HCI) has emerged as a multidisciplinary field that bridges computer science, artificial intelligence, psychology, and design to improve the interaction between humans and digital technologies. This study aims to analyze the intellectual structure, research trends, collaboration patterns, and emerging themes within the HCI literature through a bibliometric approach. Data were collected from the Scopus database using the keyword “Human–Computer Interaction” and related terms. Bibliometric analysis and visualization were conducted using VOSviewer to examine publication networks, keyword co-occurrences, citation structures, co-authorship relationships, institutional collaborations, and country-level research partnerships. The findings reveal that HCI research is centered on themes such as user interfaces, user experience, artificial intelligence, machine learning, computer vision, gesture recognition, virtual reality, and augmented reality. Overlay visualization indicates a recent shift toward advanced technologies, particularly deep learning, emotion recognition, convolutional neural networks, and intelligent interaction systems. Citation analysis identifies several highly influential publications that have shaped the theoretical and methodological foundations of the field, while collaboration analyses highlight the significant roles of the United States, China, and India, alongside leading institutions such as Carnegie Mellon University and the University of Washington. The results demonstrate that HCI research is increasingly interdisciplinary and globally collaborative, reflecting the growing demand for intelligent, adaptive, and user-centered technologies. This study provides a comprehensive overview of the evolution of HCI research and offers insights into future directions, particularly in the areas of AI-driven interaction, multimodal recognition, immersive environments, and personalized digital experiences.