---
title: "Launch and Aftermath — paper by Weber and Gruber (2026)"
description: "In November 2022, Meta's Galactica and OpenAI's ChatGPT launched about two weeks apart. 14 references."
authors: ["Maximilian Weber", "Johannes B. Gruber"]
year: 2026
venue: "Proceedings of the Seventh Workshop on Natural Language Processing and Computational Social Science"
source: "https://aclanthology.org/2026.nlpcss-1.5/"
doi: "10.18653/v1/2026.nlpcss-1.5"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/weber-2026-launch-aftermath-contrasting-social-media/"
---
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# Launch and Aftermath: Contrasting Social Media Responses to Chatbot Releases. The Cases of Meta's Galactica and OpenAI's ChatGPT

**Authors:** Maximilian Weber and Johannes B. Gruber · **Year:** 2026 · **Venue:** [Proceedings of the Seventh Workshop on Natural Language Processing and Computational Social Science](https://md.research.elamur.ai/free/en/venues/nlpcss/) ([Workshop on Natural Language Processing and Computational Social Science 2026](https://md.research.elamur.ai/free/en/venues/nlpcss/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.nlpcss-1.5/) · [PDF](https://aclanthology.org/2026.nlpcss-1.5.pdf) · [DOI 10.18653/v1/2026.nlpcss-1.5](https://doi.org/10.18653/v1/2026.nlpcss-1.5) · [OpenAlex](https://openalex.org/W7166838917)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
In November 2022, Meta's Galactica and OpenAI's ChatGPT launched about two weeks apart. Galactica was pulled within days, while ChatGPT became a mass-market success. The authors treat this as a natural comparison and ask three questions. How did Twitter conversations about the two launches differ? Did talk of harms grow over time for Galactica? Did positioning for science rather than general use change how the models were received?

They gathered English tweets through the Twitter Research API and filtered out unrelated "Galactica" mentions. They then combined three methods:
- a Twitter-tuned RoBERTa sentiment classifier;
- zero-shot harm/risk labelling with Llama 3.3;
- an LLM-driven topic pipeline with human-in-the-loop refinement.

Negative sentiment toward Galactica rose sharply within days, and harm-focused tweets came to make up most of its discussion. ChatGPT's negativity stayed fairly flat, and its discussion mixed hype, experimentation and criticism. The authors argue the gap reflects how each model was framed, not technical differences. Presenting Galactica as a trustworthy scientific tool made its hallucinations look far more serious. The lesson concerns expectation management in AI releases.

## Key points
- **Data:** 2,077 Galactica tweets (Nov 15–Dec 15, 2022) and 107,726 ChatGPT tweets (Nov 30–Dec 7, 2022), with 283 mentioning both. The main analyses cover each model's first eight days.
- **Relevance filter:** Off-topic tweets (e.g., Battlestar Galactica) were removed using snowflake-arctic-embed2 embeddings (via rollama) and a LASSO logistic regression. The classifier was trained on 250 hand-labelled tweets and reached accuracy 0.908 and macro F1 0.873.
- **Sentiment:** Measured with cardiffnlp/twitter-roberta-base-sentiment-latest.
  - Galactica's negative share went from 5.1% on day 1 to 35.6% on day 3 and peaked at 49.1% on day 5.
  - ChatGPT started at 12.0% and stayed around 17–21%.
  - Differences were significant (Fisher's exact test, BH-adjusted) on every day except launch day.
- **Harm/risk annotation:** Zero-shot labelling with Llama-3.3-70B-Instruct (4-bit NF4).
  - Agreement with two human annotators: κ 0.688 and 0.766, macro F1 0.843 and 0.883. The humans agreed with each other at κ = 0.67.
  - Harm-related Galactica tweets rose from 8.3% on day 1 to 57% by day 5.
- **Topics:** Assigned with Llama 3.3 70B via Ollama, following an adapted Pham et al. (2024) pipeline. ChatGPT was analysed on a 1,640-tweet subsample.
  - ChatGPT discussion was led by Innovation, Technical Details, Hype/Excitement and Criticism.
  - Galactica discussion was led by Criticism, Scientific Utility and AI Safety/Risks.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Johannes Gutenberg University Mainz](https://md.research.elamur.ai/free/en/universities/johannes-gutenberg-university-mainz/) — University, Mainz, Germany · 6 works · 665 citations
- [GESIS - Leibniz Institute for the Social Sciences](https://md.research.elamur.ai/free/en/labs/gesis-leibniz-institute-for-the-social-sciences/) — Research institute, Mannheim, Germany · 10 works · 5 citations

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## Cite

Cite as: Maximilian Weber and Johannes B. Gruber. 2026. Launch and Aftermath: Contrasting Social Media Responses to Chatbot Releases. The Cases of Meta's Galactica and OpenAI's ChatGPT. Proceedings of the Seventh Workshop on Natural Language Processing and Computational Social Science.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{weber2026launch,
  title = {{Launch and Aftermath: Contrasting Social Media Responses to Chatbot Releases. The Cases of Meta's Galactica and OpenAI's ChatGPT}},
  author = {Maximilian Weber and Johannes B. Gruber},
  booktitle = {Proceedings of the Seventh Workshop on Natural Language Processing and Computational Social Science},
  year = {2026},
  doi = {10.18653/v1/2026.nlpcss-1.5},
  url = {https://doi.org/10.18653/v1/2026.nlpcss-1.5}
}
```

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## References

14 references, 5 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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2. Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul Christiano, Jan Leike, and Ryan Lowe. 2022. [Train…](https://md.research.elamur.ai/free/en/papers/ouyang-2022-training-language-models-follow-instructions/)
3. Chau Minh Pham, Alexander Hoyle, Simeng Sun, Philip Resnik, and Mohit Iyyer. 2024. [TopicGPT: A prompt-based topic modeling framework](https://md.research.elamur.ai/free/en/papers/pham-2024-topicgpt-prompt-based-topic-modeling/). In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Pape…
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6. Johannes B. Gruber and Maximilian Weber. 2024. rollama: An R package for using generative large language models through Ollama. arXiv preprint. ArXiv:2404.07654 [cs]. — [arXiv:2404.07654](https://arxiv.org/abs/2404.07654) [find](https://scholar.google.com/scholar?q=Johannes+B%2E+Gruber+and+Maximilian+Weber%2E+2024%2E+rollama%3A+An+R+package+for+using+generative+large+language+models+through+Ollama%2E+arXiv+preprint%2E+ArXiv%3A2404%2E07654+%5Bcs%5D%2E+%E2%80%94+%5BarXiv%3A2404%2E07654%5D%28https%3A%2F%2Farxi)
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8. Jianghan Liu, Ziyu Shang, Wenjun Ke, Peng Wang, Zhizhao Luo, Jiajun Liu, Guozheng Li, and Yining Daniel Loureiro, Francesco Barbieri, Leonardo Neves, Luis Espinosa Anke, and Jose Camacho-Collados. 2022. [TimeLMs: Diachronic language models from Twitter](https://md.research.elamur.ai/free/en/papers/loureiro-2022-timelms-diachronic-language-models-twitter/). In Proceedings of the 60th Annual Meeting of th…
9. Adrian Rauchfleisch, Joshua Philip Suarez, Nikka Marie Sales, and Andreas Jungherr. 2025. Winning and losing with Artificial Intelligence: What public discourse about ChatGPT tells us about how societies make sense of technological change. Telematics and Informatics, 103:102344. [find](https://scholar.google.com/scholar?q=Adrian+Rauchfleisch%2C+Joshua+Philip+Suarez%2C+Nikka+Marie+Sales%2C+and+Andreas+Jungherr%2E+2025%2E+Winning+and+losing+with+Artificial+Intelligence%3A+What+public+discourse+about+ChatGPT+tells+us+about+how+societ)
10. Yash Sharma. 2025. MALTopic: Multi-Agent LLM Topic Modeling Framework. In 2025 IEEE World AI IoT Congress (AIIoT), pages 0707–0712, Seattle, WA, USA. IEEE. [find](https://scholar.google.com/scholar?q=Yash+Sharma%2E+2025%2E+MALTopic%3A+Multi%2DAgent+LLM+Topic+Modeling+Framework%2E+In+2025+IEEE+World+AI+IoT+Congress+%28AIIoT%29%2C+pages+0707%E2%80%930712%2C+Seattle%2C+WA%2C+USA%2E+IEEE%2E)
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*Updated 1 November 2026*