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.
It would be fascinating to see which words had most predictive value in each direction.
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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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Unfortunately I think those words would change with trends, maybe it also needs to account for “trendiness” of a word
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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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Think this works for code?
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