Brooke Foucault Welles

Oliver Mel Allen, Yi Zu, Milo Z. Trujillo et al.

In this work we collected and analyzed social media posts to investigate aesthetic-based radicalization where users searching for Cottagecore content may find Tradwife content co-opted by white supremacists, white nationalists, or other far-right extremist groups. Through quantitative analysis of over 200,000 Tumblr posts and qualitative coding of about 2,500 Tumblr posts, we did not find evidence of a explicit radicalization. We found that problematic Tradwife posts found in the literature may be confined to Tradwife-only spaces, while content in the Cottagecore tag generally did not warrant extra moderation. However, we did find evidence of a mainstreaming effect in the overlap between the Tradwife and Cottagecore communities. In our qualitative analysis there was more interaction between queer and Tradwife identities than expected based on the literature, and some Tradwives even explicitly included queer people and disavowed racism in the Tradwife community on Tumblr. This could be genuine, but more likely it was an example of extremists re-branding their content and following platform norms to spread ideologies that would otherwise be rejected by Tumblr users. Additionally, through temporal analysis we observed a change in the central tags used by Tradwives in the Cottagecore tag pre- and post- 2021. Initially these posts focused on aesthetics and hobbies like baking and gardening, but post-2021 the central tags focused more on religion, traditional gender roles, and homesteading, all markers of reactionary ideals.

Zhuoxu Duan, Zhengye Yang, Samuel Westby et al.

Large language models like GPT have proven widely successful on natural language understanding tasks based on written text documents. In this paper, we investigate an LLM's performance on recordings of a group oral communication task in which utterances are often truncated or not well-formed. We propose a new group task experiment involving a puzzle with several milestones that can be achieved in any order. We investigate methods for processing transcripts to detect if, when, and by whom a milestone has been completed. We demonstrate that iteratively prompting GPT with transcription chunks outperforms semantic similarity search methods using text embeddings, and further discuss the quality and randomness of GPT responses under different context window sizes.