Early ChatGPT User Portrait through the Lens of Data
Yuyang Deng Ni Zhao Xin Huang
Since its launch, ChatGPT has achieved remarkable success as a versatile
conversational AI platform, drawing millions of users worldwide and garnering
widespread recognition across academic, industrial, and general communities.
This paper aims to point a portrait of early GPT users and understand how they
evolved. Specific questions include their topics of interest and their
potential careers; and how this changes over time. We conduct a detailed
analysis of real-world ChatGPT datasets with multi-turn conversations between
users and ChatGPT. Through a multi-pronged approach, we quantify conversation
dynamics by examining the number of turns, then gauge sentiment to understand
user sentiment variations, and finally employ Latent Dirichlet Allocation (LDA)
to discern overarching topics within the conversation. By understanding shifts
in user demographics and interests, we aim to shed light on the changing nature
of human-AI interaction and anticipate future trends in user engagement with
language models.