Varun Srinivasan
@v
Warpcast now uses an ML model to order casts. If you have many unread casts, the model decides which ones go at the very top of your feed. It's main purpose is to learn your preferences and show you the most interesting stuff first. I'll go into some more details on how it works in the thread below.
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Varun Srinivasan
@v
Today's model uses 4 main features to "rank" a piece of content: 1. How often you like casts from the author 2. How often the author casts 3. How many interactions the cast has 4. How old the cast is
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Varun Srinivasan
@v
If you like someones casts a lot, their stuff will move higher in your feed. This is the primary factor used to rank your feed. The model's predictive power (y-axis) increases as the number of likes increases (x-axis).
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Varun Srinivasan
@v
If you cast a lot, the predictive power begins to diminish. A high prediction score means that the model will bump the cast up in the feeds of people who like it and down in the feeds of people who wont.
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Varun Srinivasan
@v
Importantly, this doesn't mean you should cast less! Your casts still show up in feeds, the model is just less confident about changing its position in your feed. But if you are casting a lot of stuff that doesn't get engagement, trimming some of it might help the model get your content to the right users.
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Varun Srinivasan
@v
The third factor is engagement - the more likes a cast has, the more the predictive power is. Interestingly, the relationship isn't linear. It increases from 1 to 10, then decreases till 50 and then increases again. I'm not entirely sure why this happens yet, warrants more investigation.
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Ramsey 🎩🤝
@ramsey
I have definitely noticed this. Made a "thread" a few weeks back, and used quote on my casts (and not replies). The first cast showed and got good engagement, the other 7 quotes went absolutely unnoticed. Maybe it was also "recognized2 as spam as I wrote it in .docx initially then copy pasted it for easier formatting.
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