In November 2022, Meta’s Galactica and OpenAI’s ChatGPT were released within fifteen days of each other, two transformer-based language models that were architecturally similar and built on comparable underlying technology, yet experienced starkly different outcomes. Where they diverged was not in technical kind but in domain positioning and epistemic framing: Galactica was explicitly marketed as a reliable scientific assistant, while ChatGPT was presented as a general-purpose conversational tool. Using Twitter data collected via the Twitter Research API, we conduct a comparative analysis of early social media discourse surrounding both models.Through sentiment classification, zero-shot harm and risk annotation, and LLM-based topic modeling, we find that negative sentiment escalated rapidly for Galactica while remaining comparatively stable for ChatGPT in the release period. Galactica experienced a marked escalation in criticism during its first week, eventually structuring much of the conversation. In contrast, ChatGPT’s early discourse remained more evenly distributed across hype, experimentation, practical engagement, and criticism. We argue that domain positioning and epistemic expectations, rather than any meaningful technological difference, played a central role in shaping public perception, with Galactica’s scientific presentation making its well-documented hallucinations appear far more damaging in public opinion.

From the start of the COVID-19 pandemic in Germany, different groups have been protesting measures implemented by different government bodies in Germany to control the pandemic. It was widely claimed that many of the offline and online protests were driven by conspiracy narratives disseminated through groups and channels on the messenger app Telegram. We investigate this claim by measuring the frequency of conspiracy narratives in messages from open Telegram chat groups of the Querdenken movement, set up to organize protests against COVID-19 restrictions in Germany. We furthermore explore the content of these messages using topic modelling. To this end, we collected 822k text messages sent between April 2020 and May 2022 in 34 chat groups. By fine-tuning a Distilbert model, using self-annotated data, we find that 8.24% of the sent messages contain signs of conspiracy narratives. This number is not static, however, as the share of conspiracy messages grew while the overall number of messages shows a downward trend since its peak at the end of 2020. We further find a mix of known conspiracy narratives make up the topics in our topic model. Our findings suggest that the Querdenken movement is getting smaller over time, but its remaining members focus even more on conspiracy narratives.