Investigating Quantitative-Qualitative Topical Preference: A Comparative Study of Early and Late Engagers in Japanese ChatGPT Conversations

TL;DR

This paper compares early and late engagers of ChatGPT on Japanese Twitter, revealing differences in their discussion topics, perspectives, and engagement patterns through combined quantitative and qualitative analyses.

Contribution

It introduces a dual methodology for analyzing engagement patterns and perspectives, highlighting semantic bias over tweet volume in understanding user communication differences.

Findings

Early engagers focus on technological and speculative topics.

Late engagers discuss AI capabilities and limitations.

Wider viewpoints are observed among late engagers.

Abstract

This study investigates engagement patterns related to OpenAI's ChatGPT on Japanese Twitter, focusing on two distinct user groups - early and late engagers, inspired by the Innovation Theory. Early engagers are defined as individuals who initiated conversations about ChatGPT during its early stages, whereas late engagers are those who began participating at a later date. To examine the nature of the conversations, we employ a dual methodology, encompassing both quantitative and qualitative analyses. The quantitative analysis reveals that early engagers often engage with more forward-looking and speculative topics, emphasizing the technological advancements and potential transformative impact of ChatGPT. Conversely, the late engagers intereact more with contemporary topics, focusing on the optimization of existing AI capabilities and considering their inherent limitations. Through our qualitative analysis, we propose a method to measure the proportion of shared or unique viewpoints within topics across both groups. We found that early engagers generally concentrate on a more limited range of perspectives, whereas late engagers exhibit a wider range of viewpoints. Interestingly, a weak correlation was found between the volume of tweets and the diversity of discussed topics in both groups. These findings underscore the importance of identifying semantic bias, rather than relying solely on the volume of tweets, for understanding differences in communication styles between groups within a given topic. Moreover, our versatile dual methodology holds potential for broader applications, such as studying engagement patterns within different user groups, or in contexts beyond ChatGPT.