English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This study investigates engagement patterns related to OpenAI's ChatGPT onJapanese Twitter, focusing on two distinct user groups - early and lateengagers, inspired by the Innovation Theory. Early engagers are defined asindividuals who initiated conversations about ChatGPT during its early stages,whereas late engagers are those who began participating at a later date. Toexamine the nature of the conversations, we employ a dual methodology,encompassing both quantitative and qualitative analyses. The quantitativeanalysis reveals that early engagers often engage with more forward-looking andspeculative topics, emphasizing the technological advancements and potentialtransformative impact of ChatGPT. Conversely, the late engagers intereact morewith contemporary topics, focusing on the optimization of existing AIcapabilities and considering their inherent limitations. Through ourqualitative analysis, we propose a method to measure the proportion of sharedor unique viewpoints within topics across both groups. We found that earlyengagers generally concentrate on a more limited range of perspectives, whereaslate engagers exhibit a wider range of viewpoints. Interestingly, a weakcorrelation was found between the volume of tweets and the diversity ofdiscussed topics in both groups. These findings underscore the importance ofidentifying semantic bias, rather than relying solely on the volume of tweets,for understanding differences in communication styles between groups within agiven topic. Moreover, our versatile dual methodology holds potential forbroader applications, such as studying engagement patterns within differentuser groups, or in contexts beyond ChatGPT.

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