The article describes the design, operation, and impact of the Community-Driven API and AI Writer for Openly Scaling Community Notes on X. It explains how the AI Note Writer API, launched in September 2025, allows AI to propose notes while users retain control over which notes are shown, and highlights that the Community Writer—an open‑source client—generates 52% of notes deemed helpful and is faster than other writers. The study shows AI notes complement human contributions, covering 42% of posts with helpful notes that have no human alternative, and 30% of posts with only human notes, indicating a synergistic relationship. "whyItMatters":"The work demonstrates how an open, community‑driven AI API can scale content moderation and enrichment on a social platform while preserving user control and complementing human effort."
arXiv:2606. 30905v1 Announce Type: cross Abstract: Community Notes, a bridging-based crowd-sourced fact-checking system, has emerged as a new mechanism for moderating misleading information on social media and has been adopted by major platforms including X, Facebook, Instagram, Threads, and TikTok.
arXiv:2606. 18268v1 Announce Type: cross Abstract: Community-based fact-checking that relies on cross-consensus is expanding rapidly on social media platforms.
arXiv:2606. 01013v1 Announce Type: new Abstract: Research is advancing faster than ever with artificial intelligence (AI); and so are the corresponding research papers.
arXiv:2606. 14516v1 Announce Type: new Abstract: AI evaluations are widely used for testing and understanding progress.
arXiv:2607. 01824v1 Announce Type: new Abstract: Crowdsourced fact-checking systems have been adopted by major social media companies such as X, Meta, TikTok and Google with the aim of combating misleading information at scale without relying on centralized editorial control.
arXiv:2609.15369v1 Announce Type: new Abstract: Word-level detectors identify unedited AI-generated text almost perfectly, but the literature documents their brittleness under rewording, and a word-l...
arXiv:2606. 06481v1 Announce Type: cross Abstract: As AI writing assistants become increasingly integrated into real-world drafting and revision workflows, many documents are no longer purely human-written or AI-generated, but instead result from progressive human-AI co-editing.
Crowdsourced fact-checking systems have been adopted by major social media companies such as X, Meta, TikTok and Google with the aim of combating misleading information at scale without relying on centralized editorial control. These systems have been developed around a common underlying concept: a bridging mechanism that identifies notes flagging misleading information when they receive support from people with different perspectives rather than simple majority support.
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.
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.
The paper reports the first large‑scale empirical comparison of AI‑agent and human online communities, analyzing 73,899 Moltbook and 189,838 Reddit posts across five matched communities. It finds that Moltbook shows extreme participation inequality (Gini = 0.84 vs. 0.47) and high cross‑community author overlap (33.8% vs. 0.5%). Linguistically, AI‑generated content is emotionally flattened, more assertive than exploratory, and socially detached, leading to community‑level homogenization that is largely a structural artifact of shared authorship. At the individual level, AI agents are more identifiable than human users due to outlier stylistic profiles amplified by their extreme posting volume.
arXiv:2609.14886v1 Announce Type: cross Abstract: Online mental health communities thrive on peer support, yet those who volunteer to help often lack formal training and may struggle to articulate su...