twitter / twitter/communitynotes

Note Request feature has no mechanism to prevent bad-faith submissions

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Dominant language
Python
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Merged PRs (30d)
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Description

Is your feature request related to a problem? Please describe.

The Note Request feature can be misused to suppress legitimate Community Notes. Some users submit requests with content like "NNN" (No Need Note), using the request field to argue against a note rather than to support one. Unlike regular Community Notes, where bad-faith notes can be downrated and hidden, there is no way to remove or flag a malicious note request, and no penalty is imposed on the requester.

A real example was observed where a user submitted a note request citing a Grok AI response that argued Community Notes are not appropriate for copyright violation claims, effectively discouraging contributors from writing a valid note.

Describe the solution you'd like

One or more of the following:

  • Allow contributors to rate or flag note requests so that bad-faith requests can be filtered out.
  • Introduce a penalty mechanism for requesters who repeatedly submit unhelpful or abusive requests.

Additional context
Here are some examples:
https://x.com/i/communitynotes/t/2026609294691676331
Image

Here is an example. This is just the tip of the iceberg, and there are many similar exploits in Japan.

Contributor guide

No contributing guide indexed for this repository

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Research direction

Start by reviewing the Note Request feature and how it currently handles submitted requests, then compare that with the filtering and downrating behavior for regular Community Notes. Done should include a defined way to flag or rate bad-faith requests and an agreed response to repeated abusive submissions, with the provided examples used as validation cases.

Written by the indexing model from the issue text.

Assessment

Tech stack
python
Domain
backend, content
Issue type
Feature
Difficulty
5/5
Estimated time
Over a week
Activity status
Stale
Clarity
Mostly clear
Newbie friendliness
35/100

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