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:
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.
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.
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.
Background
My Questions
Q1: How do your algorithm be evaluated for non-US nations?
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.