Unveiling the Uses of Generative Large Language: a Case Study on Twitter Data About ChatGPT

This study examines the evolving role of generative Large Language Models (LLMs). Using a data-driven approach, we collect and analyze the tasks users direct to these models, offering a quantitative and detailed perspective on their potential impact across various business domains. Drawing on a dataset of over 3.8 million tweets, we identify and categorize 31,747 unique tasks, including a focused case study on ChatGPT. Our methodology integrates two Natural Language Processing (NLP) techniques—Named Entity Recognition (NER) and BERTopic—to capture granular LLM-related tasks (NER) and organize them into business clusters (BERTopic). The findings reveal a broad range of applications, from programming assistance to creative content generation, underscoring the versatility of LLMs. The analysis highlights six emerging application areas for ChatGPT: human resources, programming, social media, office automation, search engines, and education. The study further considers the implications for innovation management, proposing a research agenda that explores how these identified areas align with the four stages of the innovation process: idea generation, screening/selection, development, and diffusion/sales/marketing.