Emerging Trends in ChatGPT Research: A Quantitative Literature Review and Clustering Analysis

Abstract

This study presents a quantitative literature review of ChatGPT research using a clustering approach to analyse knowledge flow through Main Path Analysis (MPA) and Global Main Path (GMP). We constructed a citation network from 1,147 articles published between 2015 and 2023, sourced from the Web of Science. The analysis employed edge-betweenness clustering to identify eight rapidly growing research topics: five clusters focus on ChatGPT in healthcare, two clusters on ChatGPT performance and one on ChatGPT in education. One of the most significant clusters is the impact and ethics of ChatGPT across various sectors, including healthcare, hospitality and cybersecurity, with AGR (0.47) and RGR (0.96) showing very high growth. Meanwhile, other clusters, such as AI chatbots in healthcare and trust and personalization in chatbots, demonstrate more moderate growth rates, with AGR 0.29 and 0.14 and RGR 0.59 and 0.29, respectively. Despite these varying growth rates, key challenges related to ethics, trust and personalization remain critical issues that must be addressed to ensure the successful and effective deployment of ChatGPT across diverse sectors.