On Increasing bridging/diversity property inside CN Contributors for non-US countries

Background

• In the outside of US, there are different political landscapes among nations.
• I have two questions about the notion of "political affiliation" in the following RQ1 in the original BirdWatch paper ( https://arxiv.org/pdf/2210.15723.pdf )
  • RQ1: Can we select a set of Birdwatch notes that both inform understanding (decrease propensity to agree with a potentially misleading claim) and are seen as helpful by a diverse population of users (in particular, users with diverse self-reported political affiliations)? Does algorithmic selection achieve these better than a supermajority voting baseline?

My Questions

• Q1: How do your algorithm be evaluated for non-US nations?

  • In particular,
    • How is party ID of the following form defined in the non-US countries?
    • 
    • e.g. While US and UK has the two party system, many EU nations or Asian nations like Korea or Japan have many parties in their legislative branch of the government.

• Q2: Could we increase the robustness of the bridging feature and diversity by the following selection methods of CN-raters at the preview phase at which only contributors could view and rate the proposed notes.

  • The methods:
    • 1. Build a classifier model to predict party-ID for given input user's post's(tweet's) texts to prevent lies on their true political affiliations.
    • 2. For each predicted party-ID label, select N*K users, where K is the number of party-IDs, where N is an arbitrary constant integer.
    • 3. Expose given proposed note to only the N*K users and evaluate it.
  • The expected behavior of this method: we would obtain the similar results with the following three figures in the original paper.

The issue names no repository files, tests, or entry points. Start by reviewing the linked BirdWatch paper and the existing evaluation and contributor-selection approach, then define how non-US political affiliations and the proposed classifier-based selection would be represented and evaluated. Done requires a decided, scoped implementation plan rather than open questions.