I skimmed through the study [1], and it seems that the methodology is as follows: They define a metric called "amplification" ratio for a set of tweets. Roughl

I skimmed through the study [1], and it seems that the methodology is as follows: They define a metric called "amplification" ratio for a set of tweets. Roughly, for a specific set of the tweets, it is the ratio of their "reach" in the sample of users with the chronological timeline (control, 1% of global users), to the sample of users with the ML timeline (treatment, 4% of global users). For a sample of users, reach of a set of tweets is defined as the share of the sample which encounter at least one of the tweets in the set. (the amplification metric is actually shifted so that 0% means a ratio of 1, i.e. equal reach) Then, they took a sample of right-wing and left-wing politions and media and calculated these amplification ratios (for individual accounts, and for the left-team and right-team grouped together, etc.). Generally, this "amplification" metric was larger for right-wing accounts (or groups of accounts). I think the use of that metric for measuring bias is misleading though, in the sense that they do not account for the fact that Twitter users are mostly left-wing [2], and this does significantly effect the metric they have chosen. Assume that I'm left-winger who does not follow any right-wing politions. Then probably any sensible algorithm which includes tweets in my timeline from accounts that I do not follow will increase the right-wing "amplification" metric, as it is enough for it to show me just one single tweet from a right-winger. If my understanding is correct, their measure for amplification is way too sensitive. (and it is worse when applied to understand the reach of a larger group of the accounts, as an encounter with a single tweet from any of the members of the group is counted as reach) [1]: https://cdn.cms-twdigitalassets.com/content/dam/blog-twitter/official/en_us/company/2021/rml/Algorithmic-Amplification-of-Politics-on-Twitter.pdf https://cdn.cms-twdigitalassets.com/content/dam/blog-twitter... [2]: There are many studies on this, e.g. this one by Pew Research Center: https://archive.md/iEJaq https://archive.md/iEJaq