An Australia-focused analysis of WildChat, a public dataset of real-world ChatGPT interaction logs, shows that the Australian subset is strongly action-oriented and comparatively work-oriented, with most interactions classified as doing and a majority of conversations classified as work-related.
Abstract
Generative AI chatbots are increasingly embedded in everyday life, yet most large-scale studies describe global patterns. This paper presents an Australia-focused analysis of WildChat, a public dataset of real-world ChatGPT interaction logs. Using descriptive analysis and a multi-layer classification scheme, we analysed 37,845 conversations identified as Australian, examining language diversity, work relevance, interaction intent, topic distribution, turn-taking, temporal change, work activities, and Australia-related domains. Our findings show that the Australian subset is strongly action-oriented and comparatively work-oriented, with most interactions classified as doing and a majority of conversations classified as work-related. The dataset also shows multilingual use and a growing presence of self-expression over time. Australia-related conversations frequently invoke local institutions, laws, regulators, education systems, companies, cultural references, and public services. Finally, we outline implications for future research, including local AI evaluation, multilingual participation, context-aware design, and safeguards for everyday high-stakes domains.
Public understanding of how people use LLM-based conversational AI assistants comes primarily from aggregate platform reports by OpenAI and Anthropic, which apply fixed taxonomies and inferred demographics to hundreds of millions of users and release only summary statistics that outside researchers cannot re-analyze. W...
Shreyasi Roy Chowdhury, Kiran Garimella· 4 citations
Artificial intelligence (AI) chatbots powered by natural language processing (NLP) have transformed human-computer interaction across sectors such as e-commerce, healthcare, and customer service. This paper reviews the evolution of chatbot technology, with a particular focus on the components that constitute modern sys...
S. Nalawade, H. Tapase, Shreya Jadhav· Journal of Big Data Technolo...· 0 citations
This research examined how public communication about emerging technologies is shaped through platform-specific multimodal conventions and engagement dynamics on TikTok to provide insights for stakeholders who seek to promote responsible AI communication on video-sharing platforms where algorithms shape which content i...
Xi-Ran Liu, M. S. Schäfer· Emerging Media· 1 citation
This study analyzes how a government-operated chatbot shapes citizen interactions and public value through emotional, thematic and behavioral dynamics and demonstrates how chatbot-mediated processes influence citizen experience in digital government.
Tehila Tigist Neguse, H. Gabay, Iris Reychav et al.· Transforming Government: Peo...· 0 citations
A methodology for automatically detecting shared lemmatised constructions is put forward and applied to a referential communication corpus where participants aim to identify novel objects for which no established labels exist and shows that automatically detected shared constructions offer a useful level of analysis to...
E. Ghaleb, Marlou Rasenberg, Wim Pouw et al.· 0 citations
In recent years, media attention has focused on artificial intelligence, particularly on chatbot services and generative intelligence. ChatGPT, created by OpenAI, was one of the earliest online tools and rapidly gained popularity. Users are indeed exposed to a service with privacy notifications and conditions of use th...
Jacopo Bassetta, D. Perpetuini, Maria Teresa Giusti et al.· Information· 0 citations
Related blog posts
MIT News · Artificial Intelligence· news.mit.eduOct 7, 2026
Students in MIT’s Concourse program delve deeply into the human condition, debate challenging questions, and learn to develop judgment about issues that can’t be quantified.
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduSep 30, 2026
Able to defeat top-ranked human players and more efficient than other models, the new system could help decision-makers in military maneuvers or business negotiations.