Early ChatGPT User Portrait through the Lens of Data
It provides insights into human-AI interaction trends for researchers and developers, but is incremental as it applies existing methods to new data.
This paper analyzes real-world ChatGPT datasets to understand early users' topics of interest, potential careers, and how these change over time, finding shifts in user demographics and interests through metrics like conversation turns, sentiment variations, and topic modeling.
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.