What I think I just read is that content moderation is complicated, error-prone, and expensive. So Meta is going to do a lot less of it. They'll let you self-moderate via a new community notes system, similar to what X does. I think this is a big win for Meta, because it means people who care about the content being right will have to engage more with the Meta products to ensure their worldview is correctly represent…
> I think this is a big win for Meta, because it means people who care about the content being right will have to engage more with the Meta products to ensure their worldview is correctly represented. Strong disagree. This is a very naive understanding of the situation. "Fact-checking" by users is just more of the kind of shouting back and forth that these social networks are already full of. That's why a third-party…
How can this be replicated with topics that are by definition controversial, and happening in real time? I don't know. But I don't think Meta/X have any sort of vested interest in seeing sober, fact-based conversations. In fact, their incentives work entirely in the opposite direction: the more anger/divisive the content drives additional traffic and engagement [1]. Whereas, with Wikipedia, I would argue the opposite is true: Wikipedia would never have gained the dominance it has if it was full of emotionally-charged content with dubious/no sourcing.
So I guess my conclusion from this is that I doubt any community-sourced "fact checking" efforts in-sourced from the social media platforms themselves will be successful, because the incentives are misaligned for the platform. Why invest any effort into something that will drive down engagement on your platform?
[1] Just one reference I found: https://www.pnas.org/doi/abs/10.1073/pnas.2024292118. From the abstract:
> ... we found that posts about the political out-group were shared or retweeted about twice as often as posts about the in-group. Each individual term referring to the political out-group increased the odds of a social media post being shared by 67%. Out-group language consistently emerged as the strongest predictor of shares and retweets: the average effect size of out-group language was about 4.8 times as strong as that of negative affect language and about 6.7 times as strong as that of moral-emotional language—both established predictors of social media engagement. ...