Early ChatGPT User Portrait through the Lens of Data

TL;DR

This study analyzes early ChatGPT user data to understand their interests, career implications, and how their interactions and demographics evolve over time, revealing insights into human-AI engagement.

Contribution

It provides a detailed analysis of real-world ChatGPT conversations, highlighting user interest shifts, sentiment changes, and topic trends over time.

Findings

User interests shift over time

Conversation dynamics vary with user demographics

Topics of interest evolve as users engage more

Abstract

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.