arXiv:2606. 18268v1 Announce Type: cross Abstract: Community-based fact-checking that relies on cross-consensus is expanding rapidly on social media platforms.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.
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.
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:2608.21374v1 Announce Type: new Abstract: Literature reviews are essential to scientific progress, but rigorously evaluating automatically generated reviews remains difficult because many aspec...
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.
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.
arXiv:2604. 02668v2 Announce Type: replace-cross Abstract: Large language models (LLMs) often exhibit sycophancy: agreement with user stance even when it conflicts with the model's opinion.