Idea: Divide old news stories into ones that mattered and didn't, and then use that to train a filter to use on current stories.
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Definition of "Mattered" is the definition of classifier bias.
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We've published 250K articles with attention data with a CC license if someone wants to use
@kaleidanet to do that https://kaleida.github.io/attention-index/ …Thanks. Twitter will use this to make your timeline better. UndoUndo
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Would also be fascinating to look at the early reaction. Tenor of comment, how it is covered elsewhere. Maybe: who knew it mattered then?
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Think this works for code?
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The word 'Imagine' :)
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How can you use past word trends as predictors of meaningfulness of any kind? Vocabulary and grammar are not semantics.
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Unfortunately I think those words would change with trends, maybe it also needs to account for “trendiness” of a word
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@paulg Matt Gentzkow at Stanford would be well positioned to do this if you are interested in funding the project!Thanks. Twitter will use this to make your timeline better. UndoUndo
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I think this new book called "Cassandra" would destroy your idea. Even at near-100% certainty of future events, ppl still don't listen.
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Can you link to that book? Seems interesting. Tried searching but couldn't find anything
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It's from Kevin Kelly, he was a guest on Sam Harris podcast, #84. He mentioned the book, maybe is not out yet.
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Any article with a headline containing an exclamation mark would be straight in the bin
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