Ah, as a non-Twitter user I hadn’t known about this. Neat.
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For any given note, most users have not rated that note, so most entries in the matrix will be zero, but that’s fine. The goal of the algorithm is to create a four-column model of users and notes, assigning each user two stats that we can call “friendliness” and “polarity”, and each note two stats that we can call “helpfulness” and “polarity”. The model is trying to predict the matrix as a function of these values, using the following formula:
Note that here I am introducing both the terminology used in the Birdwatch paper, and my own terms to provide a less mathematical intuition for what the variables mean
μ is a “general public mood” parameter that accounts for how high the ratings are that users give in general
is a user’s “friendliness”: how likely that particular user is to give high ratings is a note’s “helpfulness”: how likely that particular note is to get rated highly. Ultimately, this is the variable we care about. or is user or note’s “polarity”: its position among the dominant axis of political polarization. In practice, negative polarity roughly means “left-leaning” and positive polarity means “right-leaning”, but note that the axis of polarization is discovered emergently from analyzing users and notes; the concepts of leftism and rightism are in no way hard-coded.
The algorithm uses a pretty basic machine learning model (standard gradient descent) to find values for these variables that do the best possible job of predicting the matrix values. The helpfulness that a particular note is assigned is the note’s final score. If a note’s helpfulness is at least +0.4, the note gets shown.
The core clever idea here is that the “polarity” terms absorb the properties of a note that cause it to be liked by some users and not others, and the “helpfulness” term only measures the properties that a note has that caused it to be liked by all. Thus, selecting for helpfulness identifies notes that get cross-tribal approval, and selects against notes that get cheering from one tribe at the expense of disgust from the other tribe.
This is the formalization of the concept “left hand whuffy” from Charlie Stross’s “down and out in the magic kingdom”, 2003. When people who usually disagree with people like you actually agree with you or like what you’ve said, that’s special and deserves attention. I’ve always wanted to see it implemented. I don’t usually tweet but I’ll have to look at this.
Good catch. I’d genuinely misremembered. I lump the two together, but generally far prefer Stross as a storyteller, even though Doctorow’s futurism is also first-rate, in a different dimension. I found the story in Down and Out to be Stross-quality.
That sort of good idea for a social network improvement is definitely signature Doctorow, though.
https://vitalik.eth.limo/general/2023/08/16/communitynotes.html
Ah, as a non-Twitter user I hadn’t known about this. Neat.
Quote
This is the formalization of the concept “left hand whuffy” from Charlie Stross’s “down and out in the magic kingdom”, 2003. When people who usually disagree with people like you actually agree with you or like what you’ve said, that’s special and deserves attention. I’ve always wanted to see it implemented. I don’t usually tweet but I’ll have to look at this.
Down and Out in the Magic Kingdom was by Cory Doctorow, not Stross.
Good catch. I’d genuinely misremembered. I lump the two together, but generally far prefer Stross as a storyteller, even though Doctorow’s futurism is also first-rate, in a different dimension. I found the story in Down and Out to be Stross-quality.
That sort of good idea for a social network improvement is definitely signature Doctorow, though.