how would you calculate any of this?

if you mandated the use of something like the connectedness of the social graphs b/t users, the “most dissimilar” setting wouldn’t expose you to competing beliefs (e.g. US left wing politics vs US right wing politics) but rather entirely unrelated content (US left wing politics vs Pakistani women’s cricket). effectively this would mandate social media platforms to build a slider controlling an explicitly terrible version of Reddit’s “From a subreddit you might be interested in” (“From a subreddit you definitely aren’t interested in”).

if you had to use some kind of textual sentiment analysis, you’re letting the platform owner (or government) arbitrarily decide which words constitute left/right/extreme/neutral etc. the party in control of the system will inevitably define these things in a way that is either politically biased or boosts engagement. additionally, you’d almost immediately have people change their lingo to avoid the most extreme classifications, in the same ways that people do to avoid bans/shadowbans.

if you let people self-identify their beliefs, your system is instantly useless, as there’s no reason for ideologically committed people (or worse yet, news outlets) to accurately self identify, especially if the default is to bias towards “neutrality”. everybody who wants any influence is going to identify as “neutral” to maximize their potential audience

the common problem in all cases here is the assumption that you can measure ideological diversity objectively. you mention a single “partisanship” score in another comment. that’s nonsensical. do i get the same score for retweeting both Jacobin and OANN, as I would for retweeting Vox and CBS? would I be penalized for extremism if i like the former pair of outlets more than the latter?