# Public Attitudes Toward ChatGPT on Twitter: Sentiments, Topics, and Occupations

arXiv: https://arxiv.org/abs/2306.12951 · HTML: https://mlsanity.com/paper/2306.12951/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 58 references that are in the map.
- First posted on arXiv 22 Jun 2023.

## Builds on (most cited first, 19 of 19)

- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [TruthfulQA: Measuring How Models Mimic Human Falsehoods](https://mlsanity.com/paper/2109.07958/) (2021)
- [Competition-Level Code Generation with AlphaCode](https://mlsanity.com/paper/2203.07814/) (2022)
- [Ethical and social risks of harm from Language Models](https://mlsanity.com/paper/2112.04359/) (2021)
- [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022)
- [Teaching Models to Express Their Uncertainty in Words](https://mlsanity.com/paper/2205.14334/) (2022)
- [TweetEval: Unified Benchmark and Comparative Evaluation for Tweet Classification](https://mlsanity.com/paper/2010.12421/) (2020)
- [CoAuthor: Designing a Human-AI Collaborative Writing Dataset for Exploring Language Model Capabilities](https://mlsanity.com/paper/2201.06796/) (2022)
- [FOLIO: Natural Language Reasoning with First-Order Logic](https://mlsanity.com/paper/2209.00840/) (2022)
- [SemEval-2017 Task 4: Sentiment Analysis in Twitter](https://mlsanity.com/paper/1912.00741/) (2019)
- [ChatGPT: Jack of all trades, master of none](https://mlsanity.com/paper/2302.10724/) (2023)
- [Co-Writing Screenplays and Theatre Scripts with Language Models: An Evaluation by Industry Professionals](https://mlsanity.com/paper/2209.14958/) (2022)
- [A Categorical Archive of ChatGPT Failures](https://mlsanity.com/paper/2302.03494/) (2023)
- [Co-Writing with Opinionated Language Models Affects Users' Views](https://mlsanity.com/paper/2302.00560/) (2023)
- [TimeLMs: Diachronic Language Models from Twitter](https://mlsanity.com/paper/2202.03829/) (2022)
- [XLM-T: Multilingual Language Models in Twitter for Sentiment Analysis and Beyond](https://mlsanity.com/paper/2104.12250/) (2021)
- [ChatGPT: A Meta-Analysis after 2.5 Months](https://mlsanity.com/paper/2302.13795/) (2023)
- ["I think this is the most disruptive technology": Exploring Sentiments of ChatGPT Early Adopters using Twitter Data](https://mlsanity.com/paper/2212.05856/) (2022)
- [ChatGPT in education: A discourse analysis of worries and concerns on social media](https://mlsanity.com/paper/2305.02201/) (2023)

## Built on by (most cited first, 6 of 6)

- [REALM: A Dataset of Real-World LLM Use Cases](https://mlsanity.com/paper/2503.18792/) (2025)
- [Generative AI Takes a Statistics Exam: A Comparison of Performance between ChatGPT3.5, ChatGPT4, and ChatGPT4o-mini](https://mlsanity.com/paper/2501.09171/) (2025)
- [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)
- [Using Sentiment Analysis to Investigate Peer Feedback by Native and Non-Native English Speakers](https://mlsanity.com/paper/2507.22924/) (2025)
- [Benchmarking ChatGPT and DeepSeek in April 2025: A Novel Dual Perspective Sentiment Analysis Using Lexicon-Based and Deep Learning Approaches](https://mlsanity.com/paper/2509.19346/) (2025)
- [Expectation, Backlash, Recovery, and Excitement: How Model Releases Shape Reddit Perceptions of Conversational AI Systems](https://mlsanity.com/paper/2608.24654/) (2026)

## Abstract (arXiv)

ChatGPT sets a new record with the fastest-growing user base, as a chatbot powered by a large language model (LLM). While it demonstrates state-of-the-art capabilities in a variety of language-generation tasks, it also raises widespread public concerns regarding its societal impact. In this paper, we investigated public attitudes towards ChatGPT by applying natural language processing techniques such as sentiment analysis and topic modeling to Twitter data from December 5, 2022 to June 10, 2023. Our sentiment analysis result indicates that the overall sentiment was largely neutral to positive, and negative sentiments were decreasing over time. Our topic model reveals that the most popular topics discussed were Education, Bard, Search Engines, OpenAI, Marketing, and Cybersecurity, but the ranking varies by month. We also analyzed the occupations of Twitter users and found that those with occupations in arts and entertainment tweeted aboutChatGPT most frequently. Additionally, people tended to tweet about topics relevant to their occupation. For instance, Cybersecurity is the most discussed topic among those with occupations related to computer and math, and Education is the most discussed topic among those in academic and research. Overall, our exploratory study provides insights into the public perception of ChatGPT, which could be valuable to both the general public and developers of this technology.