Network Visualization of ChatGPT Research: a study based on term and keyword co-occurrence network analysis

TL;DR

This paper uses co-occurrence network analysis on 577 publications to identify major research themes related to ChatGPT, revealing key terms and their relationships to guide future research in AI and information science.

Contribution

It introduces a network visualization approach to map ChatGPT research topics based on term and keyword co-occurrence analysis.

Findings

ChatGPT is the most frequently occurring term.

Related terms include artificial intelligence, large language model, and GPT.

The study highlights key research areas in ChatGPT-related literature.

Abstract

The main objective of this paper is to identify the major research areas of ChatGPT through term and keyword co-occurrence network mapping techniques. For conducting the present study, total of 577 publications were retrieved from the Lens database for the network visualization. The findings of the study showed that chatgpt occurrence in maximum number of times followed by its related terms such as artificial intelligence, large language model, gpt, study etc. This study will be helpful to library and information science as well as computer or information technology professionals.