I think this should very valuable already in this completely local regime, however, things may get even more interesting, and to recapitulate the “collaborative filtration power” of Community Notes, Pol.is, and Viewpoints.xyz, (active) users’ feedbacks are aggregated to bubble up the best comments up for new users, or for users who choose not to vote actively to tune their predictive model well. Furthermore, when users with a similar state-space already voted positively for comments that their models didn’t predict then such comments could be shown earlier to other users in the same state-space cluster, overriding the predictions of their models.
I used to think this wouldn’t reach a critical mass of high-quality active users, but I’ve started warming up to this idea. Just yesterday I was talking to some friends who basically described how they pack-hunted to debunk right-wing political commentary on Instagram and news site. And these are Brazilian diaspora normies in their 40s, highly educated but not the highly motivated teenage nerd persona that I would normally envision as an active contributor in this kind of thing. So I think if we find a way to help people like this, who already see collaborative moderation as an important public duty, by increasing and making more visible the material impact from their contributions, we can achieve critical mass and at least initially overcome the deluge of noise that characterizes online commentary.
Same. I think the internet discussion system situation is dire enough, since Reddit’s recent API changes, and since twitter became quite hostile to free accounts, that right now a critical mass of people would actually be willing to install a browser extension to see a universal comment section.
I agree that there is a huge technical and execution/growth-hacking/product risk in making the flywheel of [BetterDiscourse] self-sustaining.
But I want to emphasize that I think the “good reactions feedback data from production use → better-trained SSM → more value to the user even in a completely “local” type of use → more data” loop is, IMO, more important initially,than the network effect (the latter is supposed to attract users who are not active voters, and revenue from them). In particular, training the original version just on LessWrong data, although it’s high-quality, may not produce a good SSM because it will likely overfit on the particular topics, language, and worldviews that appear on LessWrong, rather than, let’s say, in political discourse in Portuguese.
I used to think this wouldn’t reach a critical mass of high-quality active users, but I’ve started warming up to this idea. Just yesterday I was talking to some friends who basically described how they pack-hunted to debunk right-wing political commentary on Instagram and news site. And these are Brazilian diaspora normies in their 40s, highly educated but not the highly motivated teenage nerd persona that I would normally envision as an active contributor in this kind of thing. So I think if we find a way to help people like this, who already see collaborative moderation as an important public duty, by increasing and making more visible the material impact from their contributions, we can achieve critical mass and at least initially overcome the deluge of noise that characterizes online commentary.
Same. I think the internet discussion system situation is dire enough, since Reddit’s recent API changes, and since twitter became quite hostile to free accounts, that right now a critical mass of people would actually be willing to install a browser extension to see a universal comment section.
I agree that there is a huge technical and execution/growth-hacking/product risk in making the flywheel of [BetterDiscourse] self-sustaining.
But I want to emphasize that I think the “good reactions feedback data from production use → better-trained SSM → more value to the user even in a completely “local” type of use → more data” loop is, IMO, more important initially,than the network effect (the latter is supposed to attract users who are not active voters, and revenue from them). In particular, training the original version just on LessWrong data, although it’s high-quality, may not produce a good SSM because it will likely overfit on the particular topics, language, and worldviews that appear on LessWrong, rather than, let’s say, in political discourse in Portuguese.