# "I think this is the most disruptive technology": Exploring Sentiments of ChatGPT Early Adopters using Twitter Data

arXiv: https://arxiv.org/abs/2212.05856 · HTML: https://mlsanity.com/paper/2212.05856/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 35 references that are in the map.
- First posted on arXiv 12 Dec 2022.

## Builds on (most cited first, 3 of 3)

- [Challenges in Docker Development: A Large-scale Study Using Stack Overflow](https://mlsanity.com/paper/2008.04467/) (2020)
- [APIRO: A Framework for Automated Security Tools API Recommendation](https://mlsanity.com/paper/2201.07959/) (2022)
- [Collaborative Application Security Testing for DevSecOps: An Empirical Analysis of Challenges, Best Practices and Tool Support](https://mlsanity.com/paper/2211.06953/) (2022)

## Built on by (most cited first, 30 of 32)

- [Summary of ChatGPT-Related Research and Perspective Towards the Future of Large Language Models](https://mlsanity.com/paper/2304.01852/) (2023)
- [Evaluating ChatGPT's Information Extraction Capabilities: An Assessment of Performance, Explainability, Calibration, and Faithfulness](https://mlsanity.com/paper/2304.11633/) (2023)
- [Generative Pre-trained Transformer: A Comprehensive Review on Enabling Technologies, Potential Applications, Emerging Challenges, and Future Directions](https://mlsanity.com/paper/2305.10435/) (2023)
- [ChatGPT and a New Academic Reality: Artificial Intelligence-Written Research Papers and the Ethics of the Large Language Models in Scholarly Publishing](https://mlsanity.com/paper/2303.13367/) (2023)
- [ChatGPT: A Meta-Analysis after 2.5 Months](https://mlsanity.com/paper/2302.13795/) (2023)
- [An Empirical Study on Information Extraction using Large Language Models](https://mlsanity.com/paper/2305.14450/) (2023)
- [LLM-assisted Knowledge Graph Engineering: Experiments with ChatGPT](https://mlsanity.com/paper/2307.06917/) (2023)
- [Bias and Fairness in Chatbots: An Overview](https://mlsanity.com/paper/2309.08836/) (2023)
- [ChatAgri: Exploring Potentials of ChatGPT on Cross-linguistic Agricultural Text Classification](https://mlsanity.com/paper/2305.15024/) (2023)
- [Multidimensional Evaluation for Text Style Transfer Using ChatGPT](https://mlsanity.com/paper/2304.13462/) (2023)
- [ChatNVD: Advancing Cybersecurity Vulnerability Assessment with Large Language Models](https://mlsanity.com/paper/2412.04756/) (2024)
- [Conversational AI-Powered Design: ChatGPT as Designer, User, and Product](https://mlsanity.com/paper/2302.07406/) (2023)
- [Seeing ChatGPT Through Students' Eyes: An Analysis of TikTok Data](https://mlsanity.com/paper/2303.05349/) (2023)
- [RegimeFolio: A Regime Aware ML System for Sectoral Portfolio Optimization in Dynamic Markets](https://mlsanity.com/paper/2510.14986/) (2025)
- [Public Attitudes Toward ChatGPT on Twitter: Sentiments, Topics, and Occupations](https://mlsanity.com/paper/2306.12951/) (2023)
- [Learning to Prompt in the Classroom to Understand AI Limits: A pilot study](https://mlsanity.com/paper/2307.01540/) (2023)
- [ChatGPT v Bard v Bing v Claude 2 v Aria v human-expert. How good are AI chatbots at scientific writing?](https://mlsanity.com/paper/2309.08636/) (2023)
- [The Battle of LLMs: A Comparative Study in Conversational QA Tasks](https://mlsanity.com/paper/2405.18344/) (2024)
- [Rise of Generative Artificial Intelligence in Science](https://mlsanity.com/paper/2412.20960/) (2024)
- [ChatGPT-Crawler: Find out if ChatGPT really knows what it's talking about](https://mlsanity.com/paper/2304.03325/) (2023)
- [EmoXpt: Analyzing Emotional Variances in Human Comments and LLM-Generated Responses](https://mlsanity.com/paper/2501.06597/) (2025)
- [AI Insights: A Case Study on Utilizing ChatGPT Intelligence for Research Paper Analysis](https://mlsanity.com/paper/2403.03293/) (2024)
- [A Qualitative Study on Using ChatGPT for Software Security: Perception vs. Practicality](https://mlsanity.com/paper/2408.00435/) (2024)
- [AgenticVM: Agentic AI for Adaptive Software Vulnerability Management](https://mlsanity.com/paper/2605.01739/) (2026)
- [Exploring the Cognitive Dynamics of Artificial Intelligence in the Post-COVID-19 and Learning 3.0 Era: A Case Study of ChatGPT](https://mlsanity.com/paper/2302.04818/) (2023)
- [What Types of Questions Require Conversation to Answer? A Case Study of AskReddit Questions](https://mlsanity.com/paper/2303.17710/) (2023)
- [Leveraging Explainable AI to Analyze Researchers' Aspect-Based Sentiment about ChatGPT](https://mlsanity.com/paper/2308.11001/) (2023)
- [Winning and losing with Artificial Intelligence: What public discourse about ChatGPT tells us about how societies make sense of technological change](https://mlsanity.com/paper/2507.06876/) (2025)
- [Evaluating ChatGPT on Medical Information Extraction Tasks: Performance, Explainability and Beyond](https://mlsanity.com/paper/2601.21767/) (2026)
- [Comparative Analysis of Large Language Models in Healthcare](https://mlsanity.com/paper/2604.10316/) (2026)

## Abstract (arXiv)

Large language models have recently attracted significant attention due to their impressive performance on a variety of tasks. ChatGPT developed by OpenAI is one such implementation of a large, pre-trained language model that has gained immense popularity among early adopters, where certain users go to the extent of characterizing it as a disruptive technology in many domains. Understanding such early adopters' sentiments is important because it can provide insights into the potential success or failure of the technology, as well as its strengths and weaknesses. In this paper, we conduct a mixed-method study using 10,732 tweets from early ChatGPT users. We first use topic modelling to identify the main topics and then perform an in-depth qualitative sentiment analysis of each topic. Our results show that the majority of the early adopters have expressed overwhelmingly positive sentiments related to topics such as Disruptions to software development, Entertainment and exercising creativity. Only a limited percentage of users expressed concerns about issues such as the potential for misuse of ChatGPT, especially regarding topics such as Impact on educational aspects. We discuss these findings by providing specific examples for each topic and then detail implications related to addressing these concerns for both researchers and users.