---
title: "COMMUNITYNOTES — paper by Xing et al. (2026)"
description: "To support it they build COMMUNITYNOTES, which contains about 105K multilingual posts from public… 35 references, cited by 1 paper here."
authors: ["Rui Xing", "Preslav Nakov", "Timothy Baldwin", "Jey Han Lau"]
year: 2026
venue: "Findings of the Association for Computational Linguistics: EACL 2026"
source: "https://aclanthology.org/2026.findings-eacl.71/"
doi: "10.18653/v1/2026.findings-eacl.71"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/xing-2026-communitynotes-dataset-exploring-helpfulness-fact/"
---
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# COMMUNITYNOTES: A Dataset for Exploring the Helpfulness of Fact-Checking Explanations

**Authors:** [Rui Xing](https://md.research.elamur.ai/free/en/authors/rui-xing/), [Preslav Nakov](https://md.research.elamur.ai/free/en/authors/preslav-nakov/), [Timothy Baldwin](https://md.research.elamur.ai/free/en/authors/timothy-baldwin/) and [Jey Han Lau](https://md.research.elamur.ai/free/en/authors/jey-han-lau/) · **Year:** 2026 · **Venue:** [Findings of the Association for Computational Linguistics: EACL 2026](https://md.research.elamur.ai/free/en/venues/findings/) ([Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.findings-eacl.71/) · [PDF](https://aclanthology.org/2026.findings-eacl.71.pdf) · [DOI 10.18653/v1/2026.findings-eacl.71](https://doi.org/10.18653/v1/2026.findings-eacl.71) · [OpenAlex](https://openalex.org/W7140106845)

**Citations:** 1 citations (OpenAlex, 2026-10-14) · [1](https://md.research.elamur.ai/free/en/papers/xing-2026-communitynotes-dataset-exploring-helpfulness-fact/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Platforms such as X, Meta and TikTok increasingly rely on crowd-written notes rather than professional fact-checkers to explain why a post may mislead. On X's Community Notes, a note becomes visible only after enough diverse users rate it. This takes hours or days, and over 90% of notes are never shown. The reasons raters choose for calling a note (un)helpful also lack clear definitions. The authors frame a new task: given a post and its note, predict whether the note is helpful (binary) and why (multi-label). To support it they build COMMUNITYNOTES, which contains about 105K multilingual posts from public Community Notes releases (2021–2024), labelled with the platform's own ranking algorithm. They also propose a framework that uses automatic prompt optimization to draft and iteratively refine definitions of the reason labels, then feeds these definitions to the predictors. Baseline models handle helpfulness reasonably well but struggle with reasons. The optimized definitions improve both tasks, especially reason prediction. Helpfulness signals also benefit evidence-sufficiency prediction and fact-checking on CLIMATE-FEVER.

## Key points
- COMMUNITYNOTES holds 104,966 posts: 58,569 in English and 46,397 in other languages, with English, Japanese and Spanish the most frequent. It uses stratified 7:1:2 splits (73,472 / 10,496 / 20,998), kept separate for English and non-English data.
- The reason label set has 18 categories: 8 for helpful notes and 10 for unhelpful ones. Notes labelled NEED_MORE_RATINGS were removed from helpfulness prediction.
- Most posts (84.74%) have a single note. Of all posts, 64.36% have only helpful notes, 31.30% only unhelpful notes and 4.34% a mix. Texts are short, with median tiktoken lengths of 42 tokens for posts and 70 for notes.
- The definition-optimization pipeline was validated mainly on English. Its cross-lingual transfer remains future work, alongside multimodal extensions and human-centered evaluation.
- Code and data are released at github.com/ruixing76/Helpfulness-FCExp. The authors stress that predictions should assist human review, not drive automatic moderation.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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- [The University of Melbourne](https://md.research.elamur.ai/free/en/universities/the-university-of-melbourne/) — University, Melbourne, Australia · 100 works · 22,618 citations
- [Mohamed bin Zayed University of Artificial Intelligence](https://md.research.elamur.ai/free/en/universities/mohamed-bin-zayed-university-of-artificial-intelligence/) — University, Abu Dhabi, United Arab Emirates · 124 works · 5,676 citations

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## Cite

Cite as: Rui Xing, Preslav Nakov, Timothy Baldwin and Jey Han Lau. 2026. COMMUNITYNOTES: A Dataset for Exploring the Helpfulness of Fact-Checking Explanations. Findings of the Association for Computational Linguistics: EACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{xing2026communitynotes,
  title = {{COMMUNITYNOTES: A Dataset for Exploring the Helpfulness of Fact-Checking Explanations}},
  author = {Rui Xing, Preslav Nakov, Timothy Baldwin and Jey Han Lau},
  booktitle = {Findings of the Association for Computational Linguistics: EACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-eacl.71},
  url = {https://doi.org/10.18653/v1/2026.findings-eacl.71}
}
```

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*Updated 31 October 2026*