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

[xcsf6]

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.