arXiv:2410. 02091v4 Announce Type: replace-cross Abstract: Generative artificial intelligence (AI) facilitates content production and enhances ideation, with potentially important implications for developer productivity and participation in software development.
By Fangchen Song, Ashish Agarwal, Wen Wen
The paper examines how four stakeholder groups—AI developers, professional translators, language learners, and language service providers—discuss machine translation on social media. Using a dataset of 79,286 posts from Reddit, Facebook, Bluesky, and Mastodon (2019‑2025), the authors find frequent disagreements and strong conflicts over translation quality, efficiency, and reliability. These conflicts arise because AI communities view the issues as technical, while non‑AI users prioritize quality nuances, time savings, trust, and broader social concerns.
By Yujun Wang, Ehud Reiter, Shimei Pan, Steffen Eger, Wei Zhao
arXiv:2608. 03585v1 Announce Type: new Abstract: Open-source software communities are a form of digital public infrastructure that not only produces code, but also generates public knowledge and interpersonal relationships through visible collaboration.
By Mengying Zhou, Yongjie Yin, Yang Chen
arXiv:2609.16856v1 Announce Type: cross
Abstract: English Wikipedia is one of the largest examples of collective intelligence on the Web, sustained not only by article production but also by voluntee...
By Neal Reeves, Maja \'Swieczkowska, Amy Rechkemmer, Elena Simperl
The paper investigates how shared community affiliations, measured via Bluesky starter packs, correlate with common ground between users. By analyzing 191,648 user pairs, it finds that lexical similarity—used as a proxy for common ground—increases monotonically with the number of shared starter packs, especially when those packs represent distinct topical communities. The study also shows that this effect is independent of network proximity, indicating that community membership is a distinct, measurable carrier of common ground.
By Sagar Kumar, Lawrence Swaminathan Xavier Prince, Julia Mendelsohn, Brooke Foucault Welles, Nicholas W. Landry
Despite remarkable progress in machine translation (MT), non-AI communities have raised growing concerns about MT systems, suggesting a noticeable gap between technical advancement and the needs of real-world users. For instance, while NLP researchers focus on benchmark performance, end users care about ethical concerns, trust, reliability, costs, and more.