Document Type
Poster
Publication Date
Fall 2024
Abstract
This research presents a topic modeling analysis of the WILDCHAT-FULL dataset, examining user interactions with ChatGPT across over one million conversations. The study focused specifically on extended conversations (five or more exchanges) between U.S.-based users and ChatGPT in English. Using Latent Dirichlet Allocation (LDA), I identified 50 distinct conversational topics with coherence scores ranging from -1.5 to -14.92 (mean: -4.47). The analysis revealed diverse interaction patterns spanning creative writing, jailbreaking (attempting to get ChatGPT to do something against its guidelines), technical discussions, business applications, and educational queries. A particularly striking finding was that creative writing and role-play scenarios dominated the interactions, comprising over 25% of the identified topics.
Recommended Citation
Sussman, Hannah, "Human-Chatbot Interaction Patterns: A Topic Modeling Analysis of 3,275 Conversations with ChatGPT" (2024). IPHS 200: Programming Humanity. Paper 71.
https://digital.kenyon.edu/dh_iphs_prog/71
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.